Target-weight landscape creation for real time tracking of advertisement campaigns

ABSTRACT

A processing device selects a population of persons and measures sales metrics from the population over a time period and measures an advertising weight over the time period. The processing device determines an effect that the advertising weight has on the sales metrics and additionally calculates values for a degree of targetedness for the advertisement to the population of persons. The processing device determines an effect that the degree of targetedness has on the sales metrics and generates a multi-dimensional model that measures the combined effects of the advertising weight and the degree of targetedness on the sales metrics.

RELATED APPLICATIONS

This patent application claims the benefit under 35 U.S.C. § 119(e) ofU.S. Provisional Application No. 61/709,884, filed Oct. 4, 2012, whichis herein incorporated by reference.

TECHNICAL FIELD

Embodiments of the present disclosure relate to the field of mediaadvertising and, more particularly, to tracking and managing advertisingcampaigns.

BACKGROUND

Tracking return on investment (ROI) from television (TV) is an unsolvedproblem for advertising. There are no mechanisms that allow for trackinga viewer from a view event to a purchase in a store, dealership or overthe web. This has led to many marketers being unable to allocaterational budgets towards TV advertising. There have been many attemptsto track the revenue being generated from TV advertising. Some of theseattempts are set forth below.

A. IPTV—Many commentators have written that efforts such as internetprotocol enabled television (IPTV) will eventually enable TV conversionsto be tracked via conversion tracking pixels similar to those in placetoday throughout the web. IPTVs obtain their TV content from theinternet and use hypertext transport protocol (HTTP) for requestingcontent. However, there are many technical challenges before trackingconversions using IPTVs becomes a reality. Today, only about 8% of US TVhouseholds have IP enabled TV. Attempts to introduce IPTVs such asGoogle® TV and Apple® TV have met with only lukewarm interest. Even ifweb-like conversion tracking becomes possible using TV, it still won'tcapture all of the activity such as brand recognition leading to delayedconversions, and purchasing at retail stores.

B. RFI Systems—Some companies have experimented with methods forenabling existing TVs to be able to support a direct “purchase” from“the lounge” using present-day Set Top Box systems and remote controls.The QUBE® system, piloted in the 1970s, was an early version of this andallowed TV viewers to send electronic feedback to TV stations. Somesystem providers have developed an on-screen “bug” that appears at thebottom of the screen, and asks the consumer if they would want moreinformation or a coupon. The consumer can click on their remote controlto accept. Leading television content providers have also experimentedwith interactive capabilities. Although promising, adoption of remotecontrol RFI systems is constrained by lack of hardware support andstandards. These systems also have the same disadvantages of IPTV, inbeing unable to track delayed conversions.

C. Panels—One of the most common fallbacks in the TV arena—when facedwith difficult-to-measure effects—is to use volunteer, paid panels tofind out what people do after they view advertisements. There areseveral companies that use panels to try to track TV exposures to sales.One advantage of this method is that it makes real-time trackingpossible. However, in all cases, the small size of the panel (e.g.,25,000 people for some panels) presents formidable challenges forextrapolation and difficulty finding enough transactions to reliablymeasure sales. Another problem with the panel approach is the cost ofmaintaining the panels.

D. Mix Models—If data from previous campaigns has been collected, thenit may be possible to regress the historical marketing channel activity(e.g., impressions bought on TV ads, radio ads, web ads, print, etc.)against future sales. Unfortunately, such an approach offers no help ifthe relationships change in the future. Moreover, such an approach doesnot provide real time tracking. In addition, historical factors arerarely orthogonal—for example, retailers often execute coordinatedadvertising across multiple channels correlated in time on purpose inorder to exploit seasonal events. This can lead to a historical factorsmatrix that aliases interactions and even main effects. Even if thereare observations in which all main effects vary orthogonally, inpractice there may be too few cases for estimation.

E. Market Tests—Market Tests overcome the problems of mix models bycreating orthogonal experimental designs to study the phenomena underquestion. TV is run in some geographic areas and not others, and salesthen compared between the two. Market tests rely on local areas tocompare treatments to controls. One problem typical to market tests istheir inability to be used during a national campaign. Once a nationaltelevision ad campaign is under way, there are no longer any controlsthat aren't receiving the TV signal of the ad campaign. This causesadditional problems—for example, a market test might be executedflawlessly in April, and then a national campaign starts up in May.However, some external event is now in play during May, and the findingscompiled meticulously during April are no longer valid. This is aproblem of the market test being a “research study” that becomes “stale”as soon as the national campaign is started. Thus, market tests alsofail to provide real time tracking.

None of the above methods or techniques are able to effectively trackthe effects of TV advertising on sales in multiple channels (e.g.,retail sales, web sales, phone sales, etc.). Although television viewingmay often result in customers that visit retail stores, purchaseproducts, search on the web, or consult their mobile phones, theseconversions (e.g., sales) are generally not linkable to the TV broadcast(e.g., to the TV advertisement). For the majority of advertisers, it maybe difficult to link the customers' viewing of an ad to their decisionto purchase later through a retail store or purchase on the web, becausethese purchases are not directly attributable to the TV broadcast (e.g.,there is no direct link between the TV broadcast and the purchase).Moreover, none of the above techniques are able to perform real-timetracking across all advertiser sales channels without the use of panels.

SUMMARY

In one embodiment, an advertising (ad) campaign may be tracked inreal-time using treatment groups and control groups to determine theeffects of the advertising campaign. An experimental advertisementcampaign (also referred to as a local ad campaign) may be introduced toa treatment group. The experimental advertisement campaign may runsimultaneously with an existing advertising campaign (e.g., a nationaladvertising campaign) in the treatment group. A control group, bycontrast, may run only the existing advertising campaign. Thedemographics (e.g., the ages, nationalities, income levels, educationlevels, etc.) of the people in the treatment group and the people in thecontrol group may be similar to each other (e.g., the variation in thedemographics of the two groups may be within a certain threshold). Inaddition, the demographics of both the experimental region and thecontrol region may be similar to the demographics of a larger region(e.g., a state, a country, etc.) to which the advertising campaign isapplied. Alternatively, demographics between groups and/or regions mayvary, but be applied to a model that accounts for such variations.

By measuring the change in sales or conversions that occur in thetreatment groups when compared to the control groups, the effect of theexperimental advertising campaign on sales within the treatment groupsmay be calculated. These effects may then be extrapolated to the largerregion (e.g., to the state, to the country, etc.). This allows anadvertiser to track, in real time, the effects of an advertisingcampaign for a larger geographic region (e.g., a state, a country),using smaller regions (e.g., the treatment groups and control groups).

In another embodiment, a multi-dimensional model (also referred to as alandscape) may be generated that models the effects of advertisingweight (the amount of advertisements) and degree of targetedness (theprobability that a sale of a product or service will be made as a resultof a viewer being exposed to an advertisement) on an advertisingcampaign. The multi-dimensional model may be generated by establishingcontrol groups and treatment groups that vary from the control groupseither in degree of targetedness or advertising weight. Differences insales metrics for each of the different treatment groups and controlgroups may be used along with the known degrees of targetedness andadvertising weights associated with those treatment groups and controlgroups to develop the multi-dimensional model. The multi-dimensionalmodel may then be used to perform real time tracking of an advertisingcampaign using control groups and/or treatment groups that havedifferent degrees of targetedness and/or advertising weights from oneanother and/or from a larger region to which the advertising campaign isbeing applied.

In a further embodiment, the real-time tracking of the effects of anadvertisement campaign and/or a multi-dimensional model generated forthat advertising campaign may be used to modify and/or optimize theadvertising campaign. Such modifications and optimizations may beperformed in real time as the advertising campaign is being broadcast.The advertising campaign may be modified to meet one or more salesgoals, such as a target advertising campaign cost, a target sales perimpression, a target cost per conversion, etc. The advertising campaignmay be modified by changing the advertising weight of the advertisingcampaign and/or the degree of targetedness for the advertising campaign.After modifying the advertising campaign, the effects of the modifiedadvertising campaign can be tracked to determine whether the one or moresales goals are met.

The above is a simplified description of the disclosure in order toprovide a basic understanding of some aspects of the disclosure. Thisdescription is not an extensive overview of the disclosure. It isintended to neither identify key or critical elements of the disclosure,nor delineate any scope of the particular implementations of thedisclosure or any scope of the claims. Its sole purpose is to presentsome concepts of the disclosure in a simplified form as a prelude to themore detailed description that is presented later.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be understood more fully from the detaileddescription given below and from the accompanying drawings of variousembodiments of the present invention, which, however, should not betaken to limit the present invention to the specific embodiments, butare for explanation and understanding only.

FIG. 1 is a block diagram of a system architecture in which embodimentsof the present invention described herein may operate

FIG. 2 is a flow diagram illustrating a method of tracking and managingan advertisement campaign, according to one embodiment.

FIG. 3 is an exemplary graph illustrating a desired lift in sales abovea base line level of sales that occurs when stimuli are applied to atreatment area or tracking cell, according to one embodiment.

FIG. 4 illustrates different geographic regions which may be used totest different media concentration levels, according to one embodiment.

FIG. 5 is an exemplary graph illustrating the lift achieved fordifferent media concentrations, according to one embodiment.

FIG. 6 illustrates an exemplary map that divides the US into regionswhich have similar sales revenues.

FIG. 7 illustrates an exemplary map that divides the US into regionswhich have similar populations.

FIG. 8A is a flow diagram illustrating a method for real time trackingof an advertisement campaign, according to one embodiment.

FIG. 8B is a flow diagram illustrating another method for real timetracking of an advertisement campaign, according to another embodiment.

FIG. 9A illustrates local ad insertion during a national advertisementcampaign, in accordance with one embodiment of the present invention.

FIG. 9B is an exemplary graph that illustrates the amount of lift causedby an existing national advertising campaign and the lift caused by anexperimental ad campaign in a treatment group, according to oneembodiment.

FIG. 10 is a flow diagram illustrating a method for developing a modelfor an advertisement landscape, according to one embodiment.

FIGS. 11A-11B illustrate the application of impressions to ahypothetical campaign, according to one embodiment.

FIG. 12 illustrates various factors used by a treatment area selector,according to one embodiment.

FIG. 13A illustrates treatment areas and control areas for ahypothetical local campaign, according to one embodiment.

FIG. 13B illustrates control area selection fitness function, accordingto one embodiment.

FIGS. 14A-14B illustrate treatment area criteria and values for ahypothetical local campaign.

FIG. 15 illustrates a lift tracking report that shows treatment versescontrol over time and reports on the lift being generated, according toone embodiment.

FIG. 16 is a flow diagram illustrating a method for developing a modelfor an advertisement landscape, according to one embodiment.

FIG. 17 is a flow diagram illustrating a method for optimizing a mediacampaign, according to one embodiment.

FIG. 18 is a flow diagram illustrating a method for optimizing a mediacampaign, according to another embodiment.

FIG. 19 illustrates a diagrammatic representation of a machine in theexemplary form of a computer system.

DETAILED DESCRIPTION

Measuring the effects of TV advertising on purchases in a store oronline is difficult. Providing systems and methods to track the effectsof TV advertising may allow for better targeting, optimization, andcontrol of advertisements because of visibility into their performance.

Some of the embodiments described herein provide systems and/or methodsfor measuring the effects of TV advertising campaigns (e.g., one or moreTV advertisements/commercials) on multi-channel sales (e.g., on salesvia stores, the internet, via the phone, etc.). The systems and methodsmay also allow a user to modify or optimize TV advertising campaignsbased on advertising data (e.g., based on the results of the advertisingcampaign, such as the lift or increased sales). Additionally, thesystems and methods described herein may generate multi-dimensionalmodels (also referred to as a landscape) for an advertising campaign,which may be used to more accurately track and control the advertisingcampaign.

In some embodiments, existing cable, TV and/or satellite infrastructuremay be used to identify and select treatment groups (also referred to astracking cells, treatment areas or experimental regions) and controlgroups (also referred to as control areas, control cells or controlregions). A group may be a combination of households that are capable ofbeing served advertisements (e.g., broadcast regions, cable zones,geographic areas, demographic and/or other commonalities). Treatmentgroups are groups that will be used to run experiments, and controlgroups are groups that will be used as controls for comparison to thetreatment groups. In one embodiment, the treatment groups and/or controlgroups “mirror” a larger region to which an existing ad campaign isbeing applied (e.g., a national region) in demographics, elasticityand/or other metrics. The treatment groups may be treated with anational advertising campaign as well as additional TV advertising(referred to as a local advertisement campaign or experimentaladvertisement campaign). The local or experimental ad campaign may besimilar to what is occurring nationally from the national advertisingcampaign but at higher concentrations (e.g., more TV ads are displayed).This causes sales effects in the treatment groups to be greater inmagnitude than the surrounding control groups, which may be exposed tojust the national advertisement campaign.

Using advertising data collected in the treatment groups and the controlgroups, the systems and/or methods may extrapolate sales and costperformance of the local or experimental advertisement campaign to thenational advertisement campaign. This extrapolation may encompass salesover multiple channels, including phone sales, online sales, brick andmortar retail sales, and so forth. For example, if product sales occurthrough retail stores, retail store performance in treatment groups arecompared against control groups to determine an increase attributable tothe additional TV advertising in the treatment groups. In anotherexample, if the product is sold through the web, then increases intraffic with IP addresses coming from the treatment groups may indicatethe impact of the TV advertisements on web sales (e.g., on purchasesthrough a website). In a further example, if sales are coming indelayed, then post-advertising effects can be identified and residuallift in the treatment groups may be measured against the control groups.

Certain embodiments may provide a system and/or method for automaticallyselecting the above mentioned treatment groups and control groups. Thesystem and/or methods may also automatically calculate an appropriateadvertising weight to use for the local or experimental advertisingcampaign that is applied to the treatment groups in order to producedetectable lift in sales results over sales results of the nationalcampaign.

Certain embodiments may also provide systems and/or methods that inferor create a landscape that encapsulates the measured behavior of thetreatment groups. The landscape may be generated by using a set oftreatment groups, some of which vary from the control groups byadvertising weight and some of which vary from the control groups bydegree of targetedness.

In addition, systems and/or methods provided in certain embodiments mayallow users to define and meet several performance goals for thenational advertising campaign. Performance goals may include, but arenot limited to, cost per acquisition (CPA), a budget goal (e.g., amaximum budget), and a conversions goal (e.g., a target number of salesof a product or service). The systems and/or methods may automaticallydetermine if the performance of an existing advertising campaign isbelow one or more performance goals and may adjust advertising media(e.g., TV advertisements) nationally and/or in treatment groups. Usersmay adjust the goal settings and/or provide other criteria to thesystem. The systems and/or methods may provide reports on whether theone or more performance goals were met.

The following description sets forth numerous specific details such asexamples of specific systems, components, methods, and so forth, inorder to provide a good understanding of several embodiments of thepresent invention. It will be apparent to one skilled in the art,however, that at least some embodiments of the present invention may bepracticed without these specific details. In other instances, well-knowncomponents or methods are not described in detail or are presented insimple block diagram format in order to avoid unnecessarily obscuringthe present invention. Thus, the specific details set forth are merelyexemplary. Particular implementations may vary from these exemplarydetails and still be contemplated to be within the scope of the presentinvention.

Although references are made to a “national” advertising campaign, itshould be understood that other size and types of advertising campaignsmay be used. Examples of other classes of advertising campaigns includestate wide advertising campaigns, city wide advertising campaigns,advertising campaigns targeting specific zip codes, and so forth. Forexample, consider a political advertiser that wants to do TV advertisingfor a Presidential election. The advertiser may be currently focusing on11 battleground states, and may consider these states to be almostseparate areas that the advertiser is trying to sway. One of thebattlegrounds may be, for example, Ohio. For a campaign of this nature,mirrored tracking can be set up to provide information on how the Ohiocampaign is performing. The treatment and control groups may be cablezones, which may be relatively small regions within Ohio. An advertisingweight in the cable zones may be increased above that of the advertisingcampaign, but mirrored to the targeting (e.g., the degree oftargetedness) for the larger campaign (which is Ohio). The result isthat the cable zones—a set of well-selected local communities that matchto the demographics of Ohio in general—become mirrors for the Ohiocampaign. Based on the results in the cable zones, the advertiser canextrapolate how the advertising campaign is lifting Ohio in general.

In another example, certain retailers may only have stores in a limitednumber of states, and so the retailers may run TV advertisements inspecific local areas. The retailers can buy local broadcast mediathroughout the states that they have stores in, and then may use cablezones within the state to provide for real-time mirrored tracking on howthe stores within the state are performing.

FIG. 1 is a block diagram of a system architecture 100 in whichembodiments of the present invention described herein may operate. Thesystem architecture 100 enables an advertisement platform 115 (e.g., theLucid Commerce®-Fathom Platform®) to collect data relevant to anadvertising campaign, to set up experiments, to track an advertisingcampaign in real time, and to otherwise control an advertisementcampaign. The system architecture 100 includes an advertisement platform115 connected to platform consumers 105, agency data 110, audience data120, and advertiser data 125.

The advertisement platform 115 receives as input the agency data 110,audience data 120 and advertiser data 125. The agency data 110 mayinclude media plan data (e.g., data indicating advertisements to run,target conversions (e.g., number of sales), target audiences, a planbudget, and so forth), verification data 144 (e.g., data confirming thatadvertisements were run), and trafficking data 146 (e.g., dataindicating what advertisements are shipped to which TV stations).Preferably, all the agency data 110 about what media is being purchased,run, and trafficked to stations is collected and provided to theadvertisement platform 115 to ensure that there is an accuraterepresentation of the television media. This may include setting up datafeeds for the media plan data 142, verification data 144, andtrafficking data 146.

The advertiser data 125 includes data on sales of products and/orservices that are being advertised. Advertiser data 125 may include, forexample, call center data 152, electronic commerce (ecommerce) data 154and order management data 156. The advertisement platform 115 may set upa data feed to one or more call centers to receive accurate data aboutphone orders placed by the call centers for the advertised products orservices. Additionally, recurring data feeds may be set up with thevendor or internal system of the advertiser that records orders thatcome in from the advertiser's website (ecommerce data 154). Recurringdata feeds with the order vendor or internal system that physicallyhandles the logistics of billing and/or fulfillment may also beestablished (order management data 156). This may be used for subsequentpurchases such as subscriptions and for returns, bad debt, etc. toaccurately account for revenue. This data may also originate from one ormore retail Point of Sale systems.

The advertising platform 115 may generate a record for every caller,web-converter, and ultimate purchaser of the advertised product orserver that gets reported via the advertiser data 125. The advertisingplatform 115 may append to each record the data attributes for thepurchasers in terms of demographics, psychographics, behavior, and soforth. Such demographic and other information may be provided by databureaus such as Experian®, Acxiom®, Claritas®, etc. In one embodiment,advertiser data 125 includes consumer information enrichment data 158that encompasses such demographics, behavior and psychographicsinformation.

Audience data 120 may include viewer panel data 162, guide service data164 and/or viewer information enrichment data 166. The guide servicedata 164 may include the programming of what is going to run ontelevision for the weeks ahead. The viewer information enrichment data166 may be similar to the consumer info enrichment data 158, but may beassociated with viewers of television programming as opposed toconsumers of goods and services. A feed of such viewer data 166 mayinclude demographic, psychographic, and/or behavioral data. This feedmay be obtained using the purchases of products on television, set topbox viewer records, or existing panels.

All of the feeds of the various types of data may be received and storedinto a feed repository 172 by the advertisement platform 115. All of theunderlying data may be put into production and all of the data feeds maybe loaded into an intermediate format for cleansing, addingidentifier's, etc. Personally Identifiable Information (PII) may also beextracted from the data feeds and routed to a separate pipeline forsecure storage. The advertisement platform 115 may ingest all of thedata from the data feeds. The data may be aggregated and finalvalidation of the results may be automatically completed by theadvertisement platform 115. After this, the data may be loaded into oneor more data stores 176 (e.g., databases) for use with any upstreammedia systems. These include the ability to support media planningthrough purchase suggestions, revenue predictions, pricing suggestions,performance results, etc. Additionally, an analytics engine 174 of theadvertisement platform 115 may use the data to set up experiments,perform real time tracking of an advertisement campaign, optimize anadvertisement campaign in real time, determine a landscape for anadvertisement campaign, and so forth. In one embodiment, the analyticsengine 174 performs one or more of the methods described herein.

The platform consumers 105 may include an agency 130 (e.g., anadvertising agency) and an advertiser 132 (e.g., a manufacturer of aproduct or service who wishes to advertise that product or service).Results of the real time tracking, advertisement optimizationsuggestions, advertising models (e.g., landscapes), etc. may be providedto the platform consumers 105 to enable them to fully understand andoptimize their advertisement campaigns in real time or pseudo-real time(e.g., while the campaigns are ongoing).

FIG. 2 is a flow diagram illustrating a method 200 of tracking andmanaging an advertisement campaign, according to one embodiment. Themethod 200 may be performed by processing logic that comprises hardware(e.g., processor, circuitry, dedicated logic, programmable logic,microcode, etc.), software (e.g., instructions run on a processor toperform hardware simulation), or a combination thereof. The processinglogic is configured to track and manage an advertisement campaign suchas a national advertisement campaign. In one embodiment, method 200 maybe performed by a processor, as shown in FIG. 19. In one embodiment,method 200 is performed by an advertisement platform (e.g., by analyticsengine 174 of advertising platform 115 discussed with reference to FIG.1).

Referring to FIG. 2, the method 200 starts by selecting media types thatwill be used for testing at block 205. Media types may include, but arenot limited to: television, radio, billboards, magazines, newspapers,pay per click advertisements, banner advertisements, etc. Embodimentswill be discussed herein with reference to television advertising forconvenience. However, it should be understood that embodiments may alsoapply to advertising on other media such as radio, billboards,magazines, newspapers, and so forth. Preferably, the tested media typecorresponds to a media type of an advertisement campaign currently underway or that is to be run.

At block 210, processing logic selects a group granularity (e.g., fortreatment groups). The group may be a geographic cell or area. Differentgroup granularities may include: designated market areas (DMAs), cableoperator zones (e.g., an area serviced by a cable operator), 5-digit zipcodes, 9-digit zip codes, street address, cities, states, counties,towns, etc. In one embodiment, the group granularities may be selectedbased on one or more conditions. For example, media selected should notoverlap with each other at the selected granularity. In another example,the group granularity should be low enough to support the number oftreatment groups that the user wants to field for testing. In a furtherexample, the selected media types should be able to cover the geographicarea specified by the granularity (e.g., TV generally can't routedifferent advertisements to two different houses next to each other butTV can generally route different ads to different DMAs). In oneembodiment, the types of media selected may affect the granularity ofthe groups. For example, TV advertisements (e.g., TV airtime) aregenerally purchased by DMAs, so DMA granularity may be used if TV is aselected media type. In another example, direct mail advertisements(e.g., flyers, brochures) may be purchased by zip code, so zip codegranularity may be used for direct mail media. In a further example,billboards can be purchased at street addresses with a reasonable radius(e.g., 100 meters) for line of sight, so street address granularity maybe used for billboards.

At block 215, processing logic sets a number of treatment groups.Treatment groups may be concentration cells (e.g., treatment groups usedto track the effects of different concentrations of advertisements)and/or targeting cells (e.g., treatment groups used to track the effectsof different targeting of advertisements). As the number of treatmentgroups increase, it is possible to create a more fine-grained landscape(although this may increase costs). The number of treatment groups canbe developed heuristically or algorithmically. For example, 5 controlgroups and 2 treatment groups may be used in one embodiment.

At block 220, processing logic calculates an appropriate mediaconcentration for the treatment groups. One factor which may affect thecalculation of the media concentration may be the presence of existingnational media (e.g., the presence of an existing national advertisingcampaign on TV). Because a national advertising campaign may already bein progress (as is often the case), it may be difficult to determinewhich cross-channel sales are being caused by existing national mediaand which are being caused by an experiment (e.g., the new advertisingcampaign). The sales which are caused by an existing advertisingcampaign may be referred to as “noise.” Processing logic may calculatethe appropriate media concentration in order for the results to begreater than noise (e.g., baseline sales or results) resulting fromother advertising media that may be running already (e.g., an existingnational advertising campaign on national TV).

In addition, other factors may also affect the calculation of theappropriate media concentration. One other factor may be a rarity ofevents. For example, if a conversion or sale is generated on averageevery 10 airings of an advertisement, then a station could easily have 0sales or conversions just due to chance. Another factor is thevariability of events. For example, if sales fluctuate between 0 and 800per day, with a mean of 80, then sales of 100 for the day (which is 1.2×lift) may be due to chance. Products or services with higher standarddeviations for spot sales may require a greater difference in means toensure changes are statistically significant. Another factor is noisemedia. Noise media may be national media (e.g., a national advertisingcampaign) that continues to run during the experiment. For example, anexperimental advertising campaign in a local area might generate around2 conversions. However, if the national advertising campaign is running,it might generate an average of 100 conversions per day and typicallyvary between 80 and 120 conversions. With that amount ofnationally-generated conversions and variation, 2 additional conversionsmay not be measurably different from noise. Another factor may beconversions or sales which result from other media channels (e.g.,direct mail advertisements, web advertisements, etc.).

Processing logic may incorporate the above noise sources into a model totry to estimate the impressions needed to create a statisticallydetectable change. In order to generate enough impressions to produce adetectable lift in the local area of the experimental advertisingcampaign, processing logic may estimate the number of conversions thatwould be produced from each of these sources of noise, and then definestatistical significance threshold.

Treatment-Control Experimental Design

FIG. 3 is an exemplary graph 300 illustrating a desired lift in salesabove a base line level of sales that occurs when stimuli is applied toa treatment group (e.g., when TV advertisements are injected into atracking cell or area), according to one embodiment. The solid lineindicates sales of a product for a treatment group and the dashed lineindicates sales of a product for the control group (e.g., an area whereno advertising campaign is used or where the national advertisingcampaign is used). As shown in FIG. 3, when the stimuli is applied tothe treatment group (e.g., when the experimental advertisement campaignstarts to run), the amount sales increase from the baseline (e.g., thesolid line lifts or rises above the dotted line).

FIG. 4 illustrates different treatment groups (e.g., each quarter of thelarger squares may represent a geographical region) which may be used totest different media concentration levels, according to one embodiment.Media concentration levels “L,” “M,” and “H” are tested in treatmentgroups Exp L, Exp H, and Exp M. For each treatment group, there is acorresponding control group Con L, Con H, and Con M. As shown in FIG. 4,each treatment group is associated with an upwards or downwards arrow,which indicates whether sales in the represented geographic regionincreased or decreased. For example, treatment group Exp L has anupwards arrow indicating that sales in that treatment group increased.The length of the arrows may indicate the amount of increase/decrease insales (e.g., the longer the arrow, the more the increase/decrease). FIG.4 also includes other control groups Con 1 through Con 6, that are notpaired with a treatment group, but which may be used to help to identifyoverall trends.

FIG. 5 is an exemplary graph 500 illustrating the lift achieved fordifferent media concentrations, according to one embodiment. As shown inFIG. 5, there are three media concentrations, L, M, and H (representingby upwards arrows). Media concentration H results in the highest lift,media concentration M results in a lower lift, and media concentration Lresults in the lowest lift.

Impression Estimation

In this section we will discuss how to calculate how many impressions toapply to treatment areas in order to produce a lift that will bestatistically detectable. In TV, advertisement media is often measuredin Gross Rating Points (GRPs) per week, which is a measure of number ofimpressions that each US household would typically see from the adcampaign multiplied by 100; or alternatively Impressions per ThousandHouseholds per Week (Imp/MHH/Wk) which is much the same but isimpressions viewed per household multiplied by 1000. Assume that theadvertiser has been running media in the past with a national GRP(GRP_(N)) of 176 and that the advertiser may plan on four weeks of testor experimental media (e.g., W=4). The local area used as a treatmentgroup may have 1.2 million TV households (e.g., TVHH_(L)) and there maybe 112 million TV households (e.g., TVHH_(N)) nationally. The nationalimpressions of the media (I_(N)) may be calculated as follows:I_(N)=GRP_(N)*TVHH_(N)/100. If an advertiser is aware of the sales perimpression, then the advertiser may answer a questionnaire and indicatetheir cpi_(N) value.

If the cpi_(N) is not available, then the cpi_(N) can be inferred orcalculated from historical data. The cpi_(N) can be obtained inferredfrom the following formula:C _(N)=cpi_(N) *I _(N) +C _(nomedia,N)where C_(N) is the conversions due to all sources per week, I_(N) is thenumber of national impressions of media per week, and C_(nomedia,N) isthe number of conversions generated without any media. In oneembodiment, cpi_(N) and C_(nomedia,N) may be calculated to minimize thesquared error of observations of impressions and sales. For example, thefollowing parameters may minimize the squared error based on exemplaryhistorical data (R²=0.29): cpi_(N)=1/1408000; cpc_(N)=0;C_(nomedia,N)=66.

The impressions experienced in the targeted local area due to nationalnoise media (I_(nat,L)) may also be calculated. There may be threesources of conversions in the local area: (a) national noise conversionsdue to extant national advertising (C_(nat,L)), (b) conversionsgenerated without media (C_(nomedia,nat,L)), and (c) conversions beinggenerated due to experimental media (C_(exp,L)). I_(nat,L), C_(nat,L),C_(nomedia,nat,L), and C_(exp,L), may be calculated using the followingequations:I _(nat,L)=(TVHH_(L)/TVHH_(N))*W*GRP_(N)*10*TVHH_(N)/1000C _(nat,L)=cpi_(N) *I _(nat,L)C _(nomedia,nat,L)=(TVHH_(L)/TVHH_(N))*W*C _(nomedia,N)C _(exp,L) =I _(exp,L) *W*cpi_(N)

Based on the impressions to be injected (I_(exp,L)), the expected liftin the experimental area may be calculated using the following equation:

${{Lift}( I_{\exp,L} )} = {\frac{C_{\exp,L} + C_{{nat},L} + C_{{nomedia},{nat},L}}{C_{{nat},L} + C_{{nomedia},{nat},L}} = x}$

The statistical significance of the expected lift from runningtelevision can be estimated in several ways. A binomial probabilitydistribution may be used to estimate the probability that theexperimental media would result in this number of conversions, given asuccess rate equal roughly to the conversions per impression. The chancethat the experimental media would result in a number of conversions(e.g., Pr(Lift=x|Impressions=I_(exp,L))) may be calculated using thefollowing equation:Pr(Lift=x|Impressions=I _(exp,L))=binopdf(x*(C _(nat,L) +C_(nomedia,nat,L)),I _(nat,L) +I _(exp,L),(C _(nomedia,nat,L) +I_(exp,L)*cpi_(N))/I _(exp,L))

A normal probability density function may be used to estimate theprobability (e.g., Pr(Lift=x|Impressions=I_(exp,L))) the number ofexpected conversions results from the injection of impressions(C_(nat,L)+C_(nomedia,nat,L)+C_(exp,L)) given the variability ofconversions in the local area (σ_(L)). The standard deviation may beestimated empirically from the local daily conversions timeseries(C_(d,L)) which refers to the conversions generated on date d in localarea L. In order for the normal probability density function to be used,a time series of historical conversions per day C_(d,L) shouldpreferably be available. The probability that the number of expectedconversions results from the injection of impressions may be calculatedusing the following equations:Pr(Lift=x|Impressions=I _(exp,L))=normpdf(μ_(L) +C _(exp,L),μ_(L),σ_(L))σ_(L)=sqrt(Var(C _(d,L)))μ_(L) =C _(nat,L) +C _(nomedia,nat,L)

In one embodiment, the minimum local impression concentration thatproduces a statistically significant outcome <t is calculated using thefollowing equationI _(exp,L):min Pr(Lift=x|Impressions=I _(exp,L))<t

Table 1 below illustrates exemplary results using a binomial test. Theimpression concentrations are for 6 cells (two concentration low, twoconcentration medium, and two high concentration groups) and rangedbetween 628 and 1558, which suggested that cells would range between alift of 1.1 and 1.2, and significance of 0.34 to 0.09. The tableprovides an exemplary number of impressions per thousand households thatshould be purchased in a given treatment group in order to produce astatistically significant lift.

Using these calculated significance levels, we can now select anecessary quantity of impressions that will need to be applied into ourtreatment area in order to produce a statistically significant lift. Inone embodiment the system selects the lowest number of impressions thatwill exceed a user-defined significance threshold such as p<=0.10. Inthe example in Table 1, it suggests that impressions of 1,344Imps/MHH/Wk would need to be applied to get better significance thanp<0.10. The cost of those impressions would be approximately $387,000.

TABLE 1 Estimated Lift and Significance for local area size of San DiegoImp/MHH concentration (local area) per week Imp/MHH concentration (localarea) per week 10 510 927 1344 Cost all up for all cells (full period)$3,000 $147K $267K $387K Expected Conversions due to media (full period)0.04 1.74 3.16 4.58 Expected National Noise conversions (full period)6.00 6.00 6.00 6.00 Expected National Non-Media conversions (full 19.8019.80 19.80 19.80 period) Expected Conversions lift % in area due tomedia 1.0 1.1 1.1 1.2 Statistical Significance of Results: If Media 00.60060 0.34614 0.19255 0.09755 performs at 0x, 1x or 2x, probabilitythat this would 1 1.00000 0.34614 0.19255 0.09755 occur at random. 3x isuseful for ensuring that an 1.5 1.00000 0.34614 0.19255 0.09755 effectis detectable. Assets at 3x would be known 2 1.00000 0.48979 0.192550.09755 good performers 3 1.00000 0.48979 0.19255 0.09755

FIGS. 11A-11B illustrate the application of impressions to ahypothetical campaign 1100, according to one embodiment. Injectionlevels and outcome on a time series are shown. A success or failure isindicated based on whether a change induced by a particular injectionlevel would be statistically significant. As shown, p<0.05 may be usedas a threshold for determining whether injection levels arestatistically significant in one embodiment. In the example market, andinjection level of 1800 impressions per million households per week(imp/mhh/wk) or above achieves the p<0.05 threshold.

Treatment Area Selection

Referring back to FIG. 2, at block 225, processing logic selectstreatment groups. In one embodiment, local geographic areas are selectedfor the treatment groups. In one embodiment, in order for a geographicarea to be selected for a treatment group, the geographic area shouldnot have different factors when compared to factors of other geographicareas already selected for treatment groups or control groups. Thefactors may include, but are not limited to, pricing, promotions,in-store displays, coupons, direct mail campaigns, newspaperadvertising, email, and local TV advertising. Because there may be alarge number of local geographic areas available (e.g., there arethousands of ZIP codes, cities, streets, etc., within the United Statesalone) and promotions are often run nationally and affect marketsroughly equally, it is generally possible to identify areas that do nothave different characteristics. Selecting multiple geographic areas thathave the same factors and applying the same experimental treatment(e.g., same advertising weight) to these areas may increase theprobability that changes in sales are due the experimental treatment(e.g., the local ad campaign). For example, if two areas are similar andthe same experimental treatment is applied to both areas, and both areaslift in the same manner, then this increases the likelihood that thechanges are due to the experimental treatment, in addition to improvingthe reliability of the lift estimation. Replication may help to increasethe reliability of the results. Multiple replications may be used toincrease statistical validity and to better measure the effects of anexperimental treatment.

FIG. 12 illustrates a user interface 1200 showing various factors usedby a treatment area selector, according to one embodiment. Any of theillustrated factors may be adjusted by a user via the user interface.

There are two general ways for selecting treatment groups: 1) usingaverage areas, or 2) using behaviorally distinct areas.

Treatment Area Selection for National Average Extrapolation

When selecting treatment groups using average areas, processing logicmay create a goodness function which measures averageness of sales,geographic dispersion, and averageness of population. Areas may beselected on the basis of being as “average as possible” for a business.When extrapolating to the national level, biases between the local areaand national are minimal, and it is possible to scale-up by multiplyingby the ratio of TV households in national to the area selected.

Multiple factors may be used when selecting average areas. The firstfactor may be sales per capita. If a candidate area has sales per capita(e.g., SalesPerCapita(L)) that are higher than the national average,then it is possible that the area in question might have advertisingelasticities which are also different. In order to introduce fewerassumptions or differences into the design, processing logic may useareas which have sales per capita close to the national average. Thesales per capita may be obtained using the following equation:SalesPerCapita(L)=|C _(L)/TVHH_(L) −C _(N)/TVHH_(N)|

A second factor for selecting treatment groups may be the geographicdispersion (e.g., GeoDispersion(L1)) from other experimental areas. Inone embodiment, it may be important to avoid testing too many areaswhich are too close together. Multiple treatment groups all in the samegeneral geographic area increases the possibility that some uniquefactor in this particular region may be influencing sales andelasticities. By spreading out the treatment groups over a wider area,this possibility can be reduced. The geographic dispersion for an areaL1 may be obtained using the following equation:GeoDispersion(L1)=min EarthSurfaceDistance(L1,L2)where min EarthSurfaceDistance(L1,L2) is the minimum separation betweentwo areas L1 and L2.

A third factor for selecting treatment groups may be the geographic sizeof a region (e.g., GeoSize(L)). Smaller areas are typically cheaper touse. However, with very small areas, there may be too few people inorder to achieve statistically significant results. The statisticalsignificance of any sized area can be calculated using the followingequation:GeoSize(L)=TVHH_(L)/TVHH_(N)

A fourth factor for selecting treatment groups may be the cost for ageographic area (e.g., Cost(L)). Cheaper areas allow for more media tobe run for the same price. The product of the geographic size and CPMprovides the cost of the experiment. Areas with cheaper CPMs may bepreferred, assuming that other factors of the areas are the same. Thecost may be obtained using the following equation:Cost(L)=TVHH_(L)*CPM(L)/1000

A national advertisement campaign may inject a particular amount ofimpressions, I_(N)(N), into all national areas. Such impressions may beperformed by purchasing a collection of media assets or media assetpatterns. A media asset pattern is a block of media that may bepurchased for an advertisement, such as a rotator (e.g., M-F 6 PM-9 PMCNN) or a program (e.g., “The Family Guy”). Each media asset pattern mayhave multiple media asset pattern instances, each of which maycorrespond to a specific impression, airing or advertising event. Forexample, a media asset pattern instance may be Tuesday 8:05 PM on aspecific channel.

In one embodiment, the probability of buyer (e.g., the tratio) iscalculated based on the TV programming mix that the individual iswatching (e.g., using a direct targeting method). For example, thetratio may be calculated by determining all the programs viewed by auser and summing up the buyers in that pool and dividing by the viewers.This may indicate the probability of conversion given someone watchingexactly the same TV programming mix as the individual. In oneembodiment, the direct targeting method may calculate the tratio asfollows:

${{tratio}(i)} = {{\frac{\sum\limits_{p}{B(m)}}{\sum\limits_{p}{V(m)}}\text{:}\mspace{14mu} m} \in {M(i)}}$where i is an individual viewer (e.g., individual Set Top Box viewer)who is being scored, m is one of the media programs in the set of mediaM(i) which viewer i has watched, B(m) is the number of buyers viewingmedia program m, and V(m) is the universe of all viewers of media m.

In another embodiment, demographic targeting may be used to calculatetratio. The demographic targeting method may decompose each individualviewer into a multi-element demographic variable-value vector I (e.g., avector which includes elements such as age, income, ethnicity, etc.). Inone embodiment, the vector may have any number of elements (e.g., 400,200, etc.). The user's demographics may be compared to the demographicsof purchasers of the advertiser's product P. The demographic targetingmethod may work across all possible TV programs, regardless of thescarcity of buyers in the population. The demographic targeting methodmay calculate the tratio as follows:

${{tratio}(i)} = \frac{P \cdot I}{{P} \cdot {I}}$

A project (“p”) may refer to a product advertisement that an advertiserwould like to run on TV. Both the media asset m and the project p may berecoded into a demographic vector representing the persons who view themedia and the people who have bought the product or service beingadvertised. For convenience, Corr(m,p) is projected onto a 0.1 scalewhere 1 is most similar and 0 is not similar. As Corr(m,p) approaches 1,the probability that the two distributions come from the samedistribution may also approach 1. Several measures of distributionsimilarity between m and p may be used such as a p-value based on achi-square test, or inverse Sigmoid Euclidean distance, or correlationcoefficient (max(correlationcoefficient,0)). The choice of distributionsimilarity function is one that can be made empirically.

A sixth factor for selecting treatment groups may be the censusdisparity from the United States (US) average (e.g.,CensusDisparityFromUSAverage). The census disparity from the US averagemay be the mean absolute difference between the US population censusdemographic average and the demographic vector of a particular region. Alower value for the census disparity may be better since this mayindicate that the area is not greatly different from the US average.

Using the above factors, a weighted “goodness” score may be calculatedusing the following formula:Goodness(L)=W ₁·Cost+W ₂·MinGeoDispersion+W ₃·SalesPerCapita+W₄·tratio+W ₅·CensusDisparityFromUSAvg+W ₆·ExpectedSignificancewhere W₁ through W₆ are weight factors to apply to each of the variablesthat are used to compute the goodness function.

FIG. 13A illustrates treatment areas and control areas for ahypothetical local campaign, according to one embodiment. Thesetreatment areas and control areas may be selected according to thetechniques discussed herein. FIG. 13B illustrates control area selectionfitness function 1350, according to one embodiment. FIGS. 14A-14Billustrate treatment area criteria and values for a hypothetical localcampaign.

Treatment Area Selection for Extrapolation of Behaviorally DistinctAreas

As discussed above, processing logic may also select treatment groupsusing behaviorally distinct areas. It may be appropriate to usebehaviorally distinct areas in cases where an advertiser behavesdifferently in different geographic regions. For example, winter mayarrive earlier and last longer in higher latitude areas, leading to alonger season for winter products. In another example, the southwest ofa country may have more desert regions and garden equipment needs may bedifferent. Rural areas may have a different appetite for products thanurban areas. If these differences are large then they can be addressedby creating “sub-models” for each area and then extrapolating.

Processing logic may define a set of contiguous geographic areasx_(i)=(lat_(i), lon_(i)) each of which includes a vector of measurementsof some business metrics of interest y_(i). Processing logic may findcentroids c_(j)=(lat_(j), lon_(j)) and surrounding polygons such thatthe variation of y_(i) vectors of the geographic areas that are closestto it (forming this contiguous region) are minimized. Processing logicmay find the centroids c_(j) using the following formula:

$c_{1\mspace{14mu}\ldots\mspace{14mu} n}\text{:}\mspace{14mu}\min{\sum\limits_{1\mspace{14mu}\ldots\mspace{14mu} n}{\sum\limits_{i}{( {y_{i} - {E\lbrack y\rbrack}} )^{2}\text{:}\mspace{14mu}{\forall{i\mspace{14mu}\min\mspace{14mu}{{EarthSurfaceDist}( {x_{i},c_{j}} )}}}}}}$

Processing logic may find n contiguous regions of the US Map which havey, readings that are fairly similar. This may quantize the US map intoareas which are behaviorally similar to each other. Because the lat-loncoordinates of the regions are unrelated to the behavioral vectors y andthe relationship between the two are unknown, processing logic may use astochastic algorithm to find the best centroids.

FIG. 6 illustrates an exemplary map 600 after the US has been dividedinto regions which have similar sales revenues. The US map is dividedinto 4 regions based on revenues. Region 1 includes the west andsouthwest portions of the US and indicates areas of relatively lowincome. Region 2 includes the northern portion of the US and indicatesareas of very low revenue. Region 3 includes New York and centraleastern states and indicates areas of very high revenue. Region 4includes Florida and the southeaster portion of the US and indicatesareas of low revenue.

FIG. 7 illustrates an exemplary map 700 the US has been divided intoregions which have similar populations. The US map is divided into 4regions based on revenues. Region 1 includes the southwest portion ofCalifornia (e.g., Los Angeles) and indicates areas with high population.Region 2 and Region 3 include the western and central portion of the USand indicate areas with low population. Region 4 includes thenortheastern portion of the US and indicates areas with very highpopulation.

Referring back to FIG. 2, processing logic may extrapolate nationalestimates using behaviorally similar areas, the extrapolation is acombination of these areas and may be calculated using the followingequations where C_(L) are the conversion estimates in the local area,and C_(N) the conversions in the national area.

$C_{N} = {\sum\limits_{L}\lbrack {( \frac{{TVHH}_{L}}{\sum\limits_{L}{TVHH}_{L}} )( \frac{{TVHH}_{N}}{{TVHH}_{L}} )C_{L}^{F}} \rbrack}$In one embodiment, y may a univariate variable (e.g., y may be revenuewhich represents a metric that may be important to a business) and thenational estimate may be extrapolated using the following equation whereC_(L) is a centroid estimated by a geographic clustering algorithm, andx_(i) is the local area's geographic vector:

$C_{N} = {\sum\limits_{L}\lbrack {( {y_{L}/{\sum\limits_{L}y_{L}}} )( \frac{{TVHH}_{N}}{{TVHH}_{L}} )C_{L}^{F}} \rbrack}$${{where}\mspace{14mu} y_{L}} = {{\frac{1}{\# I}{\sum\limits_{i \in I}{y_{i}\text{:}\mspace{14mu} I}}} = {\{ i \}\min{{c_{L} - x_{i}}}}}$

Control Group Selection

At block 230 of method 200, processing logic selects control groups. Oneor more control groups may be selected for and be paired with aparticular treatment group. The control groups may be selected using acombination of criteria. A first criterion may be demographicsimilarity. Control groups may be selected if they have similardemographics to their counterpart treatment groups. In order to matchthe demographics of treatment groups, a paired t-test may be performedon the array of demographic readings for treatment groups and controlgroups.

Table 2 illustrates exemplary control groups which are selected based onage, ethnicity and income levels of people in the areas selected for thecontrol groups. Each row represents a control group and columns 2through 8 indicate the percentage of difference between the controlgroup and a corresponding treatment group. For example, Let's say thatEureka, Calif. (Eureka Calif.) is being considered as a possibletreatment area. Eureka Calif. has a 1.47% difference in the number ofmales over the age of 15, as compared to the US Population.

TABLE 2 Urban White Male Male English Under Over Avg Pct Pct Pct 15+ PctPct 35K Pct 100K Pct Avg Diff DMA2 Diff Diff Diff Diff Diff Diff DiffDiff Rank EUREKA, CA 0.0510 0.0083 0.0080 0.0147 0.0597 0.0080 0.00670.0224 1 GREAT FALLS, MT 0.0646 0.0112 0.0014 0.0096 0.0864 0.00450.0054 0.0262 2 LAKE CHARLES, LA 0.0146 0.0686 0.0005 0.0101 0.02360.0503 0.0168 0.0264 3 CHICO-REDDING, CA 0.0529 0.0247 0.0064 0.00900.0490 0.0292 0.0138 0.0264 4 GAINESVILLE, FL 0.0469 0.0613 0.00650.0100 0.0346 0.0022 0.0252 0.0267 5 WICHITA FLS, TX 0.0827 0.04820.0047 0.0003 0.0413 0.0238 0.0010 0.0289 6 AMARILLO, TX 0.0719 0.04630.0000 0.0126 0.0420 0.0365 0.0095 0.0313 7 SPOKANE, WA 0.0008 0.07200.0003 0.0041 0.0752 0.0624 0.0122 0.0324 8 MEDFORD, OR 0.0088 0.07930.0082 0.0052 0.0818 0.0375 0.0128 0.0334 9 TOPEKA, KS 0.0283 0.03630.0014 0.0029 0.0838 0.0916 0.0128 0.0367 10

A second criterion used to select control groups may be spatialproximity to treatment groups and/or to other control groups. Thecontrol groups should preferably be geographically close to thetreatment groups. For example, the control groups would preferably beneighboring DMAs or zones. This may help ensure that the treatmentgroups and control groups have similar climactic factors (temperature,precipitation), economic characteristics, population attributes, etc.Spatial proximity may minimize the influence of geographic differencesbetween paired treatment groups and control groups, and may help toimprove the quality of lift measurements. Spatial proximity can bemeasured as the Earth surface kilometer distance between each location.The Great Circle method may be used for calculating distance (inkilometers) based on latitude-longitude (lat-lon) coordinates using thefollowing equation:Distinkm(x1,x2)=acos(sin(x1·lat)*sin(x2·lat)+cos(x1·lat)*cos(x2·lat)*(cos(x2·lon−x1·lon)))*rwhere x1 and x2 are the geographic area locations in latitude-longitudeand converted to radians (e.g., xi·lat=xi·lat/(180/pi);xi·lon=xi·lon/(180/pi)) and r is the radius of the Earth in kilometers(e.g., r=6378).

A third criterion used to select control groups may be matched movementin sales. The treatment groups and control groups may both showcorrelated sales for a period prior to the start of an experiment. Forexample, if the treatment group has high sales, the control group shouldalso have high sales, and vice versa. Correlated movement (e.g.,controlling by systematic variation) may suggest that the two areas areresponding in exactly the same way to changes in environmentalconditions, promotions, and other events that can affect sales. Thecorrelated movement may be obtained using the following equations:

${R( {C_{L},C_{CON}} )} = \frac{\sum{( {C_{d,L} - {E\lbrack {C_{L}({pre})} \rbrack}} )( {C_{d,{CON}} - {E\lbrack {C_{CON}({pre})} )}} }}{\sqrt{\sum{( {C_{d,L} - {E\lbrack {C_{L}({pre})} \rbrack}} )^{2}{\sum( {C_{d,{CON}} - {E\lbrack {C_{CON}({pre})} \rbrack}} )^{2}}}}}$$\mspace{20mu}{{{t( {C_{L},C_{CON}} )} = {{R( {C_{L},C_{CON}} )}\sqrt{\frac{( {N - 2} )}{1 - {R( {C_{L},C_{CON}} )}^{2}}}}};{{DOF} = {N - 2}}}$

Generally, for each treatment group, processing logic will selectmultiple control groups (e.g., 10 control groups for comparison). Usingmore control groups than treatment groups may increase the statisticalaccuracy of tests, and may also eliminate unique factors associated withparticular control groups.

Other criteria used to select control groups may include similar storesper capita (e.g., the control group may have (a) a similar number ofstores per capita and (b) a similar mean distance to store, whencompared to the treatment group) and the inventory in stock in thegroups. The stores in the control groups should have inventory in stock.One of the problems with inventory is that some of the stores may haveinventory in stock, but others may not. Validation that stores haveproducts in-stock may be measured from inventory records, or may beapproximated by determining whether the control stores have sold theproduct.

In one embodiment, the above factors may be incorporated into a goodnessvalue (e.g., a control score). Areas with the highest goodness values(e.g., the top 10 areas) for each treatment group may be selected to bean aggregate control group. An area may be selected as a control groupfor multiple treatment groups if the area is appropriate for the each ofthose treatment groups. The goodness values for each control area may beobtained using the following equations:ControlQuality(Expi,Conj)=w1*Rrank(Expi,Conj)+w2*Distinkm(Expi,Conj)+w3*DemoSimilarity(Expi,Conj)

After obtaining the goodness value (e.g., the control score) for eachcontrol area, the areas with the top goodness values (e.g., the areaswith the top 10 good values) may be selected as the control groups.

Table 3 illustrates sample control areas for Eureka, Calif., and samplevalues use to calculate the goodness scores for the sample control areas(e.g., distance).

TABLE 3 census dist overall geoarea1 geoarea2 avgdiff rankgreatcircledistance rank rank EUREKA, CA CHICO-REDDING, CA 0.01 1 109.001 0.5 EUREKA, CA MEDFORD, OR 0.03 10 126.67 2 2.6 EUREKA, CA EUGENE, OR0.03 12 205.49 3 3.3 EUREKA, CA SPOKANE, WA 0.03 8 564.33 17 6.7 EUREKA,CA CASPER, WY 0.03 5 856.44 32 10.6 EUREKA, CA GREAT FALLS, MT 0.03 7840.89 31 10.7 EUREKA, CA GRAND JUNCTION, CO 0.04 22 828.84 30 13.4EUREKA, CA CHEYENNE, WY 0.03 15 1023.36 36 13.8 EUREKA, CA IDAHO FALLS,ID 0.04 39 628.72 20 13.8 EUREKA, CA BOISE, ID 0.05 53 436.93 11 13.9EUREKA, CA GAINESVILLE, FL 0.03 13 1031.24 38 14

At block 235, processing logic may select a pre-period. The pre-periodmay be a period of activity in time that occurs before an experimentaladvertisement campaign is applied to a treatment group. The sales duringthe pre-period may be representative of “typical” sales activity. Thepre-period may be in units of days, or in other units. The pre-periodcan be set manually by the user, or can be set automatically byprocessing logic.

Measuring Targetedness of Local and National Advertising

Our objective is to understand how targeting and ad weight relate toconversions. Targeting is challenging on TV because ads are not routedto individuals. Instead, ads are placed on specific programs, rotations,times of day, etc. Targetedness can be defined in several ways. In oneembodiment, degree of targetedness, is the percentage of viewers who arelike the converting customer. The degree of targetedness is equal to“probability of buyer”. Accordingly, the higher a targetedness ratingfor a viewer, the greater the probability that the viewer will convert.We provide two targetedness metrics: (a) Direct Targeting and (b)Demographic Targeting.

Direct Targeting looks at what known buyers (converters) of the productare watching, and then creates a probability for each media instance.The method calculates the probability of buyer given the TV programmingmix that the individual who is being scored is watching. In other words,the analytics engine reviews all programs viewed, sums up the buyers inthat pool and divides by the number of viewers, as follows:

${r( {i,\alpha,\tau} )} = \frac{{\sum\limits_{m\text{:}\mspace{14mu}{v{({i,m})}}}{\sum\limits_{j \neq i}{1\text{:}\mspace{14mu}{c(j)}}}} ⩓ {v( {j,m} )}}{\sum\limits_{m\text{:}\mspace{14mu}{v{({i,m})}}}{\sum\limits_{k \neq i}{1\text{:}\mspace{14mu}{v( {k,m} )}}}}$where  t(m) ≤ τ ⩓ t(m) ≥ τ − d/2where i is an individual set-top box viewer who is being scored, m isone of the media instances which viewer i has watched, B(m) is thenumber of buyers viewing media program m, and V(m) is the universe ofall viewers of media m. For example, if there were 10 buyers out of 100on Program A, and 1 out of 100 on Program B, and an individual viewedonly Program A and B, then their buyer probability is 5.5%.

Direct buyer probability calculation can run into difficulty when thereare few conversions. Another method is to use the demographics of mediato calculate the probability of a conversion from this media. Using thismethod, analytics engine 274 decomposes each individual set-top boxviewer into a multiple element demographic variable-value vector I,which in one embodiment is a 400 element vector. Analytics engine 274then compares the viewer demographics to the demographics of purchasersof the advertiser's product P. This method has the advantage that itwill work across all possible TV programs, regardless of the potentialsparsity of buyers in the population. Demographic targeting can becomputed according to the following equation:

${r( {i,\alpha,\tau} )} = \frac{P \cdot I}{{P} \cdot {I}}$

Mirroring of Local Advertising to National Advertising

Now that we can calculate the degree-of-targetedness of local andnational advertising, we can now calculate the targetedness of availablenational and local ad inventory. We can then select local advertisinginventory so that the overall targetedness (e.g., tratio) of the localadvertising is similar to national tratio.

Application of Advertising Weight

At block 240, processing logic applies the experimental advertisingcampaign to media at the calculated advertising weight (obtained atblock 220) and targeting settings. The advertisements may be applied tothe media by purchasing the media from different TV stations, cableoperators, satellite operators, etc. The media may be purchased manually(e.g., by calling a TV station and purchasing air time directly). Themedia may also be purchased from a publisher inventory listing. Forexample, Wide Orbit presently lists some inventory that is available tobuy, and allows companies to login and purchase the media from theirautomated system via a machine application programming interface (API).Media may also automatically be purchased (e.g., without user input) bydeclaring the intent to purchase inventory and then posting the offer tobe fulfilled. For example, a buy order (e.g., stations, days, desiredprice, etc.) may be created, and posted at a location (e.g., posted onan online server or email to an operator. Processing logic may wait forthe buy order to be fulfilled by operators (e.g., cable operators, TVstations, etc.). The buy order may include a “good through” date, wherethe buy order expires after the good through date.

At block 245, processing logic obtains sales results in the treatmentgroups and control groups, and calculates the statistical significanceof the effects (e.g., the results or sales) of the experimentaladvertising campaign. Calculating the statistical significance may allowprocessing logic to verify that a statistically significant change inthe treatment group can be detected (e.g., the change is high enough tobe detectable). Statistical significance can be calculated at multiplelevels: (1) Individual local area per week, (2) Individual local areaover multiple weeks, (3) All local areas together per week, (4) Alllocal areas together over multiple weeks, etc. The statisticalsignificance may be calculated using a variety of tests including thet-test, Wilcoxon rank sum test, etc. The change in sales for an area(e.g., the movement) may be statistically significant if the p-value(e.g., the probability that change in sales occurred by change) is belowa certain threshold (e.g., 0.05 or 0.1).

Measurement of Sales Effects in Treatment Area

Table 4 illustrates exemplary changes in a treatment group after theexperimental advertising campaign is run in the treatment groups. Asshown in Table 4, the treatment group experienced a 78% lift (e.g., a78% increase in sales) as a result of the experimental ad campaign. Thischange (e.g., the 78% lift) is statistically significant (e.g., p<0.03).

TABLE 4 ExpLift vs. Paired ExpLift vs. controlLift PairedLift Metricpre-exp during exp mean diff (%) 0% 78% meanbaseline 1 0.909126 meanexp1 1.620094 stdbaseline 0.447353 0.464427 stdexp 0.75633 1.102215 diff inmeans 0 0.710968 stderrs 0.251331 0.343671 Dof 23 23 ttest 0.500 0.025

Table 5 illustrates more exemplary changes in multiple treatment groups(e.g., Ft. Meyers, Monterey, Palm Springs, San Diego, etc.) after theexperimental ad campaign is run. As shown in Table 5, each treatmentgroup showed movement that was statistically significant.

TABLE 5 SANTA BARBRA- WEST PALM FT. MYERS - MONTEREY - PALM SAN SANMAR - BEACH - metric NAPLES2 SALINAS SPRINGS DIEGO SAN LUOB FT. PIERCEControl mean diff (%) 129% −31% 358% 201% 114% 7% 6% meanbaseline0.09090909 0.04545454 0.01136363 0.07954545 0.03409090 0.3409090916.2272727 meanexp 0.20833333 0.03125 0.05208333 0.23958333 0.072916660.36458333 17.21875 stdbaseline 0.39058940 0.29977351 0.106600350.31152538 0.18250263 0.82888047 10.6163258 stdexp 0.57886761 0.227254780.30330778 0.47561133 0.29893817 0.65082606 10.0401332 diff in means0.11742424 0.01420454 0.04071969 0.16003787 0.03882575 0.023674240.99147727 stderrs 0.07347062 0.03902253 0.03412030 0.059850760.03691533 0.10940142 1.52297929 dof 182 182 182 182 182 182 182 ttest0.05586127 0.35813698 0.11713005 0.00408928 0.14715403 0.41445990.25793002

At this point, a series of local areas (treatment groups), each of whichhas received some amount of impressions, and which has been observed tohave lifted a certain amount, have been identified. Control groups havealso been identified to help to control for a variety of factors. Theresults from these treatment groups and control groups may be used toconstruct a “media mix model” which can predict the expected number ofconversions that would be generated nationally, for any amount ofimpressions. The impact of local impressions (e.g., a local treatment)applied to treatment groups may be calculated using the difference ofdifferences (DD) function. The DD function may use the followingequation:C _(exp,L)=(Exp(2)−Exp(1))−(Con(2)−Con(1))

However, the general DD function may have certain limitations. Whenestimating conversions experimentally, the local treatment groups may besmall, and may have different sizes when compared to the control groups,and to the ultimate target area to which the advertisement campaign willeventually be injected (e.g., the national level or area). For example,the control groups might average 10 conversions per week and grew by 1conversion per week. The treatment group might average only 1 conversionper week and grew by 0.1 conversions. Using the standard DD function,the treatment group would be assumed to undergo a 1 conversion upwardmovement (the same as control group), and so it would appear that thetreatment group actually shifted downwards by −0.9 conversions per week.In actuality, the treatment group grew at about the same rate as thecontrol group (e.g., at around +10% for both). As a result, modelconstruction should be performed in a way that is scale invariant. Theinherent “conversion generating capacity” of each area/group should befactored into the model because it is likely that the areas/groups willnot have the same size. Accordingly, in some embodiments other methodsmay be used to calculate the impact of local impressions applied totreatment groups.

One technique that may be used to calculate the impact of localimpressions is the DD per capita method. Several units may be used bythe DD per capita method, including conversions per capita, conversionsper TV household (TVHH), conversions per retail store, and evenconversions per square kilometer. Of these, conversions per TV Householdmay be easier to use for TV advertising because each TV household is apotential generator of conversions. The conversions units may benormalized so that they are expressed in terms of conversions per TVHH.This may be scaled to any experimental area of interest by multiplyingthe estimate for the area by the number of TVHHs in the area.

In one embodiment, the DD per capita method calculates lift as anincrease in per capita conversions per period. The DD per capita methodmay calculate the change in conversions per capita in control groups.This change is subtracted from any movement in conversions per capita intreatment groups. The remainder of conversions per capita in treatmentgroups are the conversions per capita that occurred in a treatment groupthat could not be accounted for by changes in the control groups. Aftercalculating the new conversions per capita in treatment groups due tothe experimental ad campaign, an extrapolation of conversions is thenmade to the national ad campaign by multiplying by the national TVHHs.The DD per capita method may use the following equations:Δq _(N)(t ₁ ,t ₂)=ΔQ _(N)(t ₁ ,t ₂)·TVHH_(N)  (1)ΔQ _(N)(t ₁ ,t ₂)=1/JΣ _(j=1) ^(J)[s(d _(j))·ΔΔQ _(L)(d _(j) ,t ₁ ,t₂)]  (2)ΔQ _(L)(d _(j) ,t ₁ ,t ₂)=[Q(d _(j) ,t ₂)−Q(d _(j) ,t ₁)]−[1/JΣ _(i=1)^(J)[Q(D _(i)(d _(j)),t ₂)−Q(D _(i)(d _(j)),t ₁)]]  (3)Q(d,t)=q(d,t)/TVHH_(d)  (4)where Q(d,t)=q(d,t)/TVHH_(d)=conversions per capita in treatment group dduring time-period t, where q(d,t) is the quantity of conversionsgenerated in treatment group d in time-period t appropriately normalizedto a quantity per unit of time (e.g. a day), where d_(j) is the jthlocal treatment group selected so as to be matched to national, whereD_(i)(d) is the ith local control group matched to d, where t₂ is atime-period during the experimental ad campaign, and where t₁ is priorto the experimental ad campaign. Each of the time-periods represents acertain amount of time during which measurement is taken. For example,t₁ might span two months prior to the experiment start, and t₂ might bethe first week of the experimental ad campaign. Quantities have may benormalized to equivalent units so that different lengths of time do notincrease the quantities. TVHH_(d) are the number of TV Households intreatment group d. TVHH_(N)=112,000,000 are the number of TV Householdsnationally.

The above formulas may be modified to provide different estimates fornational conversions. For example, instead of using the mean in equation(2), the media may be used. Similarly, instead of using the mean inequation (3), the media may be used. The DD per capita method above usesthe average per capita change observed in multiple areas, and implicitlygives equal weight to each treatment group. However some areas could belarge and others could be small. Rather than averaging treatment groupper capita increases as is done in equation (2) the treatment groupconversions may be summed first and then divided by sum of TVHouseholds. This may have the effect of causing larger TVHH areas toexert more influence on the estimate of per capita increase.

A second method to calculate the impact of local impressions is adifference in lifts method. The difference in lifts method may normalizeboth treatment and control group conversions so that each conversionreading is in units of the group's own average pre-experimentconversions per week average. There may be some difference in thebehavior and/or means of different groups, and these inherentdifferences are all being “normalized out” so that the group is onlybeing measured against its own performance during a pre-experimentalperiod. The difference in lifts method may also make predictions for anyexperimental area or group of interest. The estimate of percent changemay be converted into conversions by multiplying with the pre-experimentperiod average to predict actual conversions in the area or group ofinterest. The difference in lifts method may look for an increase inconversions, as compared to the typical conversion-generatingperformance of each group. The difference in lifts method may usehistorical data to calculate typical or baseline performance, and thenmay look for changes compared to that historical baseline. Control groupincreases (or decreases) are first calculated, and then the increase ordecrease for the treatment groups are calculated. The excess treatmentgroup percentage change compared to control group may be due to theintervention and is then equal to the lift due to experimental adcampaign. The number of national conversions that, when multiplied bythe lift, reach the actual number observed are the national conversionsnot due to the ad campaign. The excess that when added reach observednational conversions are due to the ad campaign. The difference in liftsmethod may use the following equations:

${\Delta\;{q_{N}( {t_{1},t_{2}} )}} = {{q_{N}( t_{2} )} - \frac{q_{N}( t_{2} )}{1 + {\%\mspace{14mu}{q_{N}( {t_{1},t_{2}} )}}}}$${\%\mspace{14mu}{q_{N}( {t_{1},t_{2}} )}} = {\frac{1}{J}{\sum\limits_{j = 1}^{J}\lbrack {{{s( d_{j} )} \cdot \%}\mspace{14mu}{q_{L}( {d_{j},t_{1},t_{2}} )}} \rbrack}}$${\%\mspace{14mu}{q_{L}( {d_{j},t_{1},t_{2}} )}} = {\lbrack \frac{q( {d_{j},t_{2}} )}{q( {d_{j},t_{1}} )} \rbrack - \lbrack {\frac{1}{I}{\sum\limits_{i = 1}^{I}\lbrack \frac{q( {{D_{i}( d_{j} )},t_{2}} )}{q( {{D_{i}( d_{j} )},t_{1}} )} \rbrack}} \rbrack}$

A third method to calculate the impact of local impressions is theconversions per capita method. If there are no background or organicconversions, then there is no background control movement to compareagainst and remove. This may be the case when advertisers are runningTV, toll free numbers, or otherwise have little brand name recognitionin an area. In this case, the effect from TV can be directly measured bythe number of conversions per capita observed in a particular group orarea using the following equations:

Δ q_(N)(t₁, t₂) = Δ Q_(L)(t₁, t₂) ⋅ TVHH_(N)${\Delta\;{Q_{L}( {d_{j},t_{1},t_{2}} )}} = \frac{Q( {d_{j},t_{2}} )}{{TVHH}_{L}}$

Automatic Adjustment to Impressions to Achieve Statistically MeasurableLift

At block 250, processing logic may adjust the media concentration (e.g.,the amount of TV advertisements, also known as the advertising weight)in areas where the change in sales does not meet a threshold forstatistical significance (e.g., p>0.05). Processing logic may adjust themedia concentration using the following formulas:I(d,t)=I(d,t)*αI(d,t)=I(d,t)+εwhere I(d, t) is the advertising weight (e.g., the impressionconcentration) in a location “d” at a time “t.” If the threshold forstatistical significance is met, then processing logic may automaticallydecrease the concentration applied to the areas that meet the thresholdfor statistical significance using the following formulas:I(d,t)=I(d,t)/αI(d,t)=I(d,t)−ε

At block 260, processing logic may generate or update a model mappingadvertising weight and degree of targetedness to sales metrics for thenational ad campaign and/or for the experimental ad campaign.

At block 265, processing logic extrapolates measured lift in a treatmentgroup over the control group to the national ad campaign. As discussedearlier, there may be an existing national advertising campaigncurrently running. Processing logic may calculate the amount of liftthat is due to the existing national advertising campaign in order todetermine the lift in a treatment group that is due to the experimentalad campaign.

At block 270 processing logic may optimize targeting based on currentperformance.

Automatic Optimization of National Campaign Constraints Based on SalesPerformance

One method of optimizing targeting uses tcpm to adjust to the empiricalovershoot or undershoot to converge on the correct tcpm to achieve thedesired CPA goal. The tcpm may be a measure of the cost per targetedimpression and may be defined as follows:tcpm(m,p)=cpm(m)/tratio(m,p)

The following equations may be used to optimize targeting using tcpm:If CPAdesired<CPAactual thentcpmtarget=tcpmactual*(CPAdesired/CPAactual)If CPAdesired>CPA then tcpmtarget=tcpmactual*(CPAactual/CPAdesired)

A second method for optimizing targeting may increase or decrease tratio(targetedness) in order to converge to the goal.If CPAdesired<CPAactual thentratiotarget=tratioactual/(CPAdesired/CPAactual)If CPAdesired>CPA then tratiotarget=tratioactual/(CPAactual/CPAdesired)

Optimization of Advertising Plan

Media is then selected according to the Greedy Algorithm below, wheretratiotarget and tcpmtarget are constraints which are set using theabove algorithm, which is dependent upon current performance. Ourobjective is to select a set of TV media into which to insert an ad,such that advertiser value per dollar is maximized. Let M_(i) be acontiguous segment of time in the TV MPEG video stream that a station isoffering for sale, CPM(M_(i)) be the cost per thousand impressions ofthe timeslot, r(M_(i)) be the degree of targetedness and l(M_(i)) be theimpressions for the timeslot. The objective is to select a set of Mediawhich maximizes:

$\sum\limits_{i}{{r( M_{i} )} \cdot {I( M_{i} )}}$subject  to${{\sum\limits_{i}{{{CPM}( M_{i} )} \cdot {{I( M_{i} )}/1000}}} \leq B};$V({M_(i)}) = true;${\frac{\sum\limits_{i}{{{CPM}( M_{i} )} \cdot {{I( M_{i} )}/1000}}}{\sum\limits_{i}{{r( M_{i} )} \cdot {I( M_{i} )}}} \leq {tcpmtarget}};$$\frac{\sum\limits_{i}{{r( M_{i} )} \cdot {I( M_{i} )}}}{\sum\limits_{i}{I( M_{i} )}} \leq {tratiotarget}$

Where B is the television campaign budget, V determines if the set ofmedia violates rotation rules (such as running an ad more than once per60 minutes, having greater than 5% of budget on any one network orday-part, and so on). Rotation rules are defined by television adbuyers.

A greedy strategy for allocating television media is to select media inorder of value per dollar, as follows:

$M_{i}\text{:}\mspace{14mu}\max\;\frac{r( M_{i} )}{{CPM}( M_{i} )}$

This may be subject to the rotation rule constraints until the budget isfilled.

The operations of blocks 270 and 275 may be performed together to finetune a national advertisement campaign in order to achieve a user'sgoals. Techniques for optimizing an advertising campaign are discussedin greater detail below with reference to FIGS. 17-18.

Referring back to FIG. 2, at block 280, and as described in the GreedyAlgorithm above, processing logic may select media that is at or lowerthan the desired tcpm and purchases the media (e.g., purchases the mediafrom a cable or satellite operator). Processing logic may additionallyselect media that corresponds to the determined degree of targetednessand advertising weight. Advertisements from the national advertisementcampaign may then be shown on the purchased media.

At block 285, the media characteristics of the experimental or localadvertisement campaign is updated to match the national mediacharacteristics, which was updated in blocks 270-280. This may includeselecting media for the local advertisement campaign in the treatmentgroups so that the tratio and tcpm in the treatment groups matches thatfor the national ad campaign. This may help to ensure that localperformance is representative of national performance.

At block 290, the processing logic determines whether the national adcampaign has ended. If the ad campaign is ended, the method ends. If thead campaign is ongoing, then the method may return to block 240, andadditional operations may be performed to continue to track the adcampaign in real time. Various analyses, measurements, models, andreports may be generated using the above formulas, data, metrics, lifts,and other information described above. These analyses and reports may beuseful in changing or optimizing a media campaign.

Mirrored Tracking

We will now describe in more detail an embodiment of the method that isdesigned to track national campaign lift using local tracking areas.FIG. 8A is a flow diagram illustrating a method 800 for real timetracking of an advertisement campaign, according to another embodiment.The method 800 may be performed by processing logic that compriseshardware (e.g., processor, circuitry, dedicated logic, programmablelogic, microcode, etc.), software (e.g., instructions run on a processorto perform hardware simulation), or a combination thereof. Theprocessing logic is configured to track and manage an advertisementcampaign. In one embodiment, method 800 may be performed by a processor,as shown in FIG. 19.

During a national television ad campaign, which is injecting I_(N)(N)impressions into all national areas, it may be desirable to measure itsmulti-channel effects Q(N). In one embodiment, local ad insertion isperformed to add additional impressions I_(L)(d) to some treatmentgroups (e.g., local areas) d. In one embodiment, additional impressionsare added carefully to maintain homogeneity between national and localad viewers. Such a process is referred to herein as “mirroring,” andinvolves careful matching of treatment groups to national viewership.Such a method may be performed using existing TV capabilities in orderto create mirrored treatment groups. Alternatively, treatment groups maynot be mirrored, and may be applied to a multi-dimensional model thatadjusts for differences between the treatment group and the nationalviewership.

For national ad insertion, advertisements can be inserted into anational video stream by electronically or manually trafficking the adsand rotation logic to one or more Networks (e.g., ABC, CBS, Fox, NBS,CW, etc.) or cable stations (e.g., CNN, SciFi, HBO, etc.) directly. TVads can be purchased for programs, rotations, run of station, and soforth.

For local Cable station ad insertions, the Cable MSO itself inserts thead into the video stream, and has 2 minutes per hour of possible ads toinsert, so approximately 13% of ad inventory. The MSO has multiplelevels of signal control. This includes the Cable Interconnect ƒ, whichcover Direct Marketing Association (DMA) areas or zones. Each zone maybe a collection of about 10,000 households.

For local Broadcast station ad insertion, local ad insertion is handledby the local station (e.g., KOMO). 4 minutes per hour of time areprovided for local station IDs, as well as local ads, so about 26% of adinventory is available to be purchased locally. There are approximately2,000 cable zone areas that can be purchased, and over 2,500 localbroadcast stations, providing considerable ability to createrepresentative treatment mirrors.

FIG. 9A illustrates local ad insertion during a national advertisementcampaign, in accordance with one embodiment of the present invention. Asshown, an advertisement may be shown nationally, such as on CNN. Thesame advertisement may also be shown locally at a DMA or interconnect,at a head end, or at a cable zone. Therefore, viewers of the DMA,interconnect, head end or cable zone will be presented with both thenational advertisement and the local advertisement.

Referring back to FIG. 8A, at block 803 of method 800 processing logicselects a local sub-population that is a part of a national population.The local sub-population is selected so as to match the nationalpopulation based on a fitness function that incorporates one or moremeasures comprising at least one of sales per capita, demographiccomposition, television penetration, satellite penetration or cablepenetration. At block 805, during a national advertisement campaign fora product or service processing logic introduces a local advertisementcampaign for the product or service to a treatment group that includesthe local sub-population. The local advertisement campaign may mirrorthe national advertisement campaign.

At block 810, processing logic measures sales of the product or servicein a control group that is based on the national advertisement campaign.The control group may be selected using previously discussed techniques.At block 815, processing logic measures sales of the product or servicein the treatment group. At block 820, processing logic calculates adifference in sales metrics between the treatment group and the controlgroup. At block 825, processing logic uses the difference in the salesmetrics to then estimate an effect on the national sales metrics due tothe national campaign.

FIG. 9B is an exemplary graph 900 that illustrates the amount of liftcaused by an existing national advertising campaign and the lift causedby the experimental ad campaign in a local treatment area or group,according to one embodiment.

As discussed above, during a national television ad campaign, which isinjecting I_(N)(N) impressions into all national areas, it may bedesirable to measure its multi-channel effects Q(N). In one embodiment,local ad insertion is performed to add additional impressions I_(L)(d)to some treatment groups (e.g., local areas) d. There are 2,000 cableand broadcast areas d available. In one embodiment, an objective forselecting good treatment groups is to find areas that match nationalwell enough so that they allow for accurate extrapolation to national.In one embodiment, the local areas and national are preferablyhomogenous populations, and so ads displayed locally have the sameeffect as is occurring nationally.

Treatment Area Selection Criteria

FIG. 8B is a flow diagram illustrating a method 850 for real timetracking of an advertisement campaign, according to another embodiment.The method 850 may be performed by processing logic that compriseshardware (e.g., processor, circuitry, dedicated logic, programmablelogic, microcode, etc.), software (e.g., instructions run on a processorto perform hardware simulation), or a combination thereof. Theprocessing logic is configured to track and manage an advertisementcampaign. In one embodiment, method 850 may be performed by a processor,as shown in FIG. 19.

At block 855 of method 800, processing logic selects one or moretreatment groups. These treatment groups may be selected using one ormore selection algorithms. Some selection algorithms may incorporatepreviously selected treatment groups. Accordingly, in one embodimentmultiple treatment groups are selected serially.

Multiple criteria may be applied for selection of treatment groups. Afirst criterion may be a low census disparity from a national average.The mean absolute difference between the ith US population censusdemographic x_(i)(N), and the demographic reading x_(i)(d) of aparticular region d in one embodiment is preferably as low as possible.A lower value indicates that the area is not greatly different from theUS average. Zip-code-level demographics are publicly available from theUS Census Bureau and these can be aggregated to the same level as thecable and broadcast systems. In the formula below w_(i) is a weightapplied to each demographic.

${m_{1}(d)} = {\sum\limits_{i}{w_{i} \cdot {{{x_{i}(d)} - {x_{i}(N)}}}}}$

Another criterion may be average sales per capita. If a candidatetreatment group has sales per capita that are higher than the nationalaverage, then it is possible that the area in question might haveadvertising elasticities which are also different. In order to introducefewer assumptions or differences into the design, in one embodiment wewill therefore favor areas which have sales per capita close to thenational average.Q(d)=q(d)/TVHH(d)=conversions per capita in area dwhere q(d) is the quantity of conversions generated in area d, TVHH(d)are the number of TV Households in area d, and TVHH(N)=112,000,000 arethe number of TV Households nationally. The following equation maycapture this criterion.m ₂(d)=|Q(d)−Q(N)|

Another criterion may be TV media targeting. The targeting of the mediabeing through the local injection systems needs to match the media beingpurchased nationally. Targeting is measured by the demographicviewership match between media and the product demographics r(d). Thefollowing equation may capture this criterion.

The degree of targetedness can be equal to “probability of buyer”. Onemethod calculates the probability of buyer given the TV programming mixthat the individual who is being scored is watching. In other words, theanalytics engine reviews all programs viewed, sums up the buyers in thatpool and divides by the number of viewers, as follows:

${r( {i,\alpha,\tau} )} = \frac{{\sum\limits_{m\text{:}\mspace{14mu}{v{({i,m})}}}{\sum\limits_{j \neq i}{1\text{:}\mspace{14mu}{c(j)}}}} ⩓ {v( {j,m} )}}{\sum\limits_{m\text{:}\mspace{14mu}{v{({i,m})}}}{\sum\limits_{k \neq i}{1\text{:}\mspace{14mu}{v( {k,m} )}}}}$where  t(m) ≤ τ ⩓ t(m) ≥ τ − d/2where i is an individual set-top box viewer who is being scored, m isone of the media instances which viewer i has watched, B(m) is thenumber of buyers viewing media program m, and V(m) is the universe ofall viewers of media m. For example, if there were 10 buyers out of 100on Program A, and 1 out of 100 on Program B, and an individual viewedonly Program A and B, then their buyer probability is 5.5%.

Another method is to use the demographics of media to calculate theprobability of a conversion from this media. Using this method,analytics engine 274 decomposes each individual set-top box viewer intoa multiple element demographic variable-value vector I, which in oneembodiment is a 400 element vector. Analytics engine 274 then comparesthe viewer demographics to the demographics of purchasers of theadvertiser's product P. This method has the advantage that it will workacross all possible TV programs, regardless of the potential sparsity ofbuyers in the population. Demographic targeting can be computedaccording to the following equation:

${r( {i,\alpha,\tau} )} = \frac{P \cdot I}{{P} \cdot {I}}$

Now that we can calculate the degree-of-targetedness of local andnational advertising, we can now calculate the targetedness of availablenational and local ad inventory. We can then select local advertisinginventory so that the overall targetedness (e.g., tratio) of the localadvertising is similar to national tratio.

Our treatment are fitness function then includes the degree ofdifference in media targeting between the local treatment area and thenational area where the national campaign is running. As the differencein targeted between the two areas diminishes, the treatment area and themedia selected for that treatment area score better in terms of fitness.m ₃(d)=|r(d)−r(N)|

Another criterion may be high geographic dispersion from other treatmentgroups. It may be useful to avoid areas which are too close together.Multiple test cells (treatment groups) all in the same generalgeographic area increases the threat that some unique factor in thisparticular region is influencing sales and elasticities. By spreadingout the test cells over a wider area, this threat can be reduced. Inaddition, increasing the dispersion of tracking cells also even helpsavoid spillover of TV broadcasts into neighboring areas, avoidingcontamination of other treatment cells. Let the set of possiblegeographic areas be G, and already selected areas S≤G. Processing logicmay use the Great Circle method to find the closest already-selectedtreatment area in Earth Surface distance kilometers, and report this asdispersion from previously selected areas. In the definition below,latitude and longitude are both converted from Cartesian to radians;where

${d_{lat} = \frac{d_{lat}}{180/\pi}},$and K=6378 is the Earth radius in kilometers. The following equationsmay capture this criterion.m ₀(d _(j))=min(ESD(d _(j) ,e):∀eεd _(1 . . . j−1))ESD(d,e)=a cos [sin(d _(lat))sin(e _(lat))]+cos d _(lat) cos e _(lat)cos e _(lon) −d _(lon) ·K

Another criterion may be low cost. Cheaper areas allow for more media tobe run for the same price. Prices of areas are available from companieswhich monitor the clearing price of all ad buys on TV. Smallergeographic areas tend to be less in demand and have lower prices, and soare favored for testing over areas such as New York in some embodiments.The following equations may capture this criterion.m ₄(d)=TVVH(d)·CPM(d)/1000

Another criterion may be cable and satellite penetration. Some areas ofthe country have lower numbers of cable TVs. In some embodiments,processing logic attempts to avoid selecting areas with unusually lowcable adoption rates. The following equations may capture thiscriterion.m ₅(d)=|pen(d)−pen(N)|<PEN

Another criterion may be number of insertable networks. Insertablenetworks are stations that can have ads inserted to them. If the numberof insertable networks becomes too low, then local inventory may not beable to match national. The following equations may capture thiscriterion.m ₆(d)=sgn(insert(d)≥INS)

Treatment Area Selection Fitness Function

Using some or all of the factors described above, a weighted fitnessscore may be calculated for each candidate treatment group. Iterativerecalculation may be performed since the GeoDispersion metric isdependent upon areas that have already been selected. In the formulabelow, R converts the raw number into a percentile.

$d_{j}\text{:}\mspace{14mu}{\min( {{\sum\limits_{k}^{\;}\;{{M_{k} \cdot {R( {m_{k}( d_{j} )} )}}\text{:}\mspace{14mu} d_{j}}} \notin d_{{1\mspace{14mu}\ldots\mspace{14mu} j} - 1}} )}$

Control Area Selection

At block 860, processing logic selects one or more control groups usingone or a set of fitness criteria. In one embodiment, one or more controlgroups are selected for and paired with each treatment group.Additionally, one or more general control groups may be selected thatare not paired to any treatment groups. The control groups enableprocessing logic to measure treatment change in quantity per capitaversus control change in quantity per capita over the same period oftime. In order for this comparison to show differences due to TV (andnot other factors), it is preferable in some embodiments to ensure thatthe control group purchase behavior, demographics, and responsiveness toadvertising are all as close as possible to the treatment groups. Someor all of the following criteria may be used to attempt to ensurehomogeneity across multiple dimensions between the control and treatmentgroups.

A first criterion may be demographic similarity. Controls shouldpreferably have similar demographics to their corresponding treatmentgroup. The D_(j)th area to be selected has the following matchdifference:

${u_{1}( {D_{j},d} )} = {\sum\limits_{i}^{\;}\;{{( {\frac{1}{J}{\sum\limits_{j = 1}^{\;}\;{x_{i}( D_{j} )}}} ) - {x_{i}(d)}}}}$

A second criterion may be geographic proximity. Where-as treatmentgroups were ideally geographically dispersed, the control groups shouldpreferably be geographically close to their treatment groups. This helpsto ensure that treatment and control areas have the same climacticfactors (temperature, precipitation), economic characteristics,population attributes, and so on. The following equation captures thiscriterion.u ₂(D _(j) ,d)=ESD(D _(j) ,d)

A third criterion may be matched movement. In one embodiment, thecontrol and treatment groups should both show coordinated movement insales for an extended period prior to the start of the experiment. Whenthe treatment group has high sales, the control group should have highsales, and vice versa. Systematic variation is a strong test forrelatedness since it suggests that the two areas are responding in thesame way to changes in environmental conditions, promotions, and otherevents that can affect sales. In the definition below, the error isproportional to the absolute difference between treatment and the sum ofcontrol areas by day. The difference of differences method will alsoscale the error by national sales, and so we also multiply the differentby national Q(N,t)^(η).

${u_{3}( {D_{j},d} )} = {\sum\limits_{t}^{\;}\;{{Q( {N,t} )}^{\eta} \cdot \lbrack {{Q( {d,t} )} - \frac{\sum\limits_{j}^{\;}\;{q( {D_{j},t} )}}{\sum\limits_{j}^{\;}\;{{TVHH}( {D_{j},t} )}}} \rbrack^{\delta}}}$

Control Area Selection Fitness Function

In one embodiment, unlike treatment groups, control groups do notrequire the purchase of any local media. Accordingly, the boundariesthat may restrict the selection of treatment groups may not apply tocontrol group selection. This is useful because it means that controlscan be selected at a finer-grain than the treatments. Treatmentsutilized 2,000 zones, averaging about 55,000 TV households each.Controls can be built from over 30,000 zip codes, averaging just 3,800TV Households. Accordingly, processing logic may assemble a set ofcontrol groups that match very precisely the demographics of thetreatment groups.

The algorithm for selecting controls may be iterative, similar totreatment group selection. However, in one embodiment, multiple controlgroups are selected for each treatment group. The set of control groupsmay be assembled to collectively match the treatment group. In oneembodiment, a best matching control group is selected. Say that thiscontrol group matches well, but has too few African Americans. Whenselecting the next control group that is being matched to the treatmentgroup, the error function is the match between the total controls (allcontrol groups selected for that treatment group), including the newcandidate control group and the originally selected control group. As aresult, if one candidate control group causes the African American quotato move closer to the treatment group, then this control group will befavored. As a result, the iterative procedure may self-correct bysuccessively selecting areas which together have demographics and saleswhich match the treatment group. Control groups D(d) may be selected foreach treatment group (d) based on the following algorithm:

$D_{j}\text{:}\mspace{14mu}{\min( {{\sum\limits_{k}^{\;}\;{{U_{k} \cdot {R( {u_{k}( D_{j} )} )}}\text{:}\mspace{14mu} D_{j}}} \notin {S\bigwedge D_{J}} \notin D_{{1\mspace{14mu}\ldots\mspace{14mu} j} - 1}} )}$

Calculate Impressions to Apply

At block 865, processing logic determines an advertising weight to applyto the treatment groups for an experimental or local ad campaign thatwill be run in parallel to the national ad campaign. This is calculatedusing methods described earlier.

Apply Advertising Impressions

At block 870, advertisements are applied to the treatment groups. Atblock 875, processing logic measures advertising results for thetreatment groups and for the control groups. At block 880, processinglogic then determines sales metrics (e.g., lift) attributable to thenational ad campaign.

Calculate Local Lift

The objective may be to create a detectable increase in the sales percapita in the treatment groups, compared to the control groups. Based onthe size of this increase, processing logic can measure how well TVadvertising is driving sales, and then estimate the unknown (butsimultaneously executing) national effects. Let ΔQ_(N)(d,t₁,t₂) be thequantity per capita per week that is occurring in a local area d betweentime t₁ and t₂ due to I_(N) impressions of national TV. Let E be thequantity per capita per week that is occurring in a local area d withoutTV. The total quantity that is observed in area d is thereforeQ_(N+)(d,t₁,t₂)=E+ΔQ_(N). The quantity per capita per week produced byI_(N) is an unknown function ƒ, such that:Q _(N+)(d,t ₁ ,t ₂)=ΔQ _(N)(d,t ₁ ,t ₂)+E=ƒ(I _(N))+E

In order to make the quantity measurable, processing logic will injectan additional amount of local impressions per capita per week I_(L) intothe treatment group d using local ad insertion systems, which produce alocal revenue per capita per week of ΔQ_(L)(d,t₁,t₂). LetΔQ_(L+)(d,t₁,t₂) be the total revenue now observed in the treatmentgroup inclusive of local and national ads, whereQ_(L+)(d,t₁,t₂)=ΔQ_(L)(d,t₁,t₂)+ΔQ_(N)(d,t₁,t₂)+E=ƒ(I_(N))+E. We nowhave:ΔQ _(L)(d,t ₁ ,t ₂)=Q _(L+)(d,t ₁ ,t ₂)−Q _(N+)(d,t ₁ ,t ₂)

Calculate National Lift

In the above formula, the quantity Q_(L+) per capita per week in thetreatment group is observable. The value Q_(N+) is not directlyobservable. However, processing logic can use the performance observableat the control groups D(d) that are matched to the treatment group todetermine Q_(N+). This results in the following equation:ΔQ _(L)(d,t ₁ ,t ₂)=Q _(L+)(d,t ₁ ,t ₂)−Q _(N+)(D(d),t ₁ ,t ₂)where Q_(L+) and Q_(N+) are both observable and an impressionconcentration (ad weight) of ΔI_(L)(d) has been used. This provides anobservation between impressions and quantity at a point higher than thenational impressions. From this, the national impressions can beinferred.

In one embodiment, in order to infer ΔQ_(N), which is running withΔI_(N)(N), it can be useful to know something about the shape of the TVimpression to quantity function ƒ Ordinarily, a linear assumption for anadvertising response would lead to unrealistically optimistic estimates.However, since processing logic is extrapolating downwards, thediminishing returns observation does not render the estimateunrealistic. Assuming diminishing returns as advertising increases, alinear fit to the observed data actually becomes a lower bound on thelift produced by the national ad impressions. Accordingly, an estimateof the function can be computed as follows:

Δ Q_(L) = f(I_(L)); Δ Q_(L) = cI_(L); $c = \frac{\Delta\; Q_{L}}{I_{L}}$

The estimate for national quantity per capita per week ΔQ_(N), can thenbe calculated based on empirical observations in the treatment groupsand control groups. Assuming multiple controls per treatment groupD_(i)(d_(j)), and multiple treatment groups d_(j), and assuming s(d_(j))is a scale-up relating to the difference in cable penetration betweenthe groups, we have the following:

$\mspace{79mu}\begin{matrix}{{\Delta\;{Q_{N}( {t_{1},t_{2}} )}} = {c \cdot I_{N}}} \\{= {\frac{1}{J}{\sum\limits_{j = 1}^{J}\;{\lbrack {{s( d_{j} )} \cdot \frac{\Delta\;{Q_{L}( {d_{j},t_{1},t_{2}} )}}{\Delta\; I_{L}}} \rbrack \cdot I_{N}}}}}\end{matrix}$       if  Δ Q_(N) ≤ 0       then      Δ Q_(N) = 0      if     Δ Q_(N) ≥ Q_(N+)      then      Δ Q_(N) = Q_(N+)$\frac{\Delta\;{Q_{L}( {d_{j},t_{1},t_{2}} )}}{\Delta\; I_{L}} = \frac{\lbrack {{Q( {d_{j},t_{t}} )} - {Q( {d_{j},t_{1}} )}} \rbrack - \lbrack {{\frac{1}{I}{\sum\limits_{i = 1}^{I}\;{Q( {{D_{i}( d_{j} )},t_{2}} )}}} - {Q( {{D_{i}( d_{j} )},t_{1}} )}} \rbrack}{\lbrack {{I( {d_{j},t_{2}} )} - {I( {d_{j},t_{1}} )}} \rbrack - \lbrack {{\frac{1}{I}{\sum\limits_{i = 1}^{I}\;{I( {{D_{i}( d_{j} )},t_{2}} )}}} - {I( {{D_{i}( d_{j} )},t_{1}} )}} \rbrack}$

FIG. 15 illustrates a lift tracking report 1500 that shows treatmentverses control over time and reports on the lift being generated,according to one embodiment. The lift tracking report 1500 may be shownin a user interface that includes multiple different drop down menusthat enable a user to change one or more properties for tracking lift.

FIG. 10 is a flow diagram illustrating a method 1000 for developing amodel for an advertisement landscape, according to one embodiment. Themethod 1000 may be performed by processing logic that comprises hardware(e.g., processor, circuitry, dedicated logic, programmable logic,microcode, etc.), software (e.g., instructions run on a processor toperform hardware simulation), or a combination thereof. The processinglogic is configured to generate a multi-dimensional model (e.g., anadvertisement landscape). In one embodiment, method 1000 may beperformed by a processor, as shown in FIG. 19.

What-if Analysis

It may be useful for an advertiser to be able to conduct “what-if”analyses on what would happen if the advertiser used different mediaconcentrations and degrees of targeting in their advertising campaign.In one embodiment, in order to provide this information to the user, aninterpolated or multi-dimensional model of the discrete measured cellsmay be developed. This multi-dimensional model may be an “advertisementlandscape” may be general function mapping from any impressionconcentration (e.g., advertising weight) and tratio to a prediction ofthe expected incremental sales due to television media on each channel.

Target-Weight Model Construction

In the preceding discussion we discussed calculation of national andlocal lift, and how this can be done by direct observation ofexperimental markets. We will now discuss the construction of atarget-weight model which combines degree-of-targeting with advertisingweight. In order to create this landscape, we will use the observedlocal impressions and local lift to form data points to infer a generalfunction mapping ad weight and targeting to conversions.

The target-weight landscape is one which makes predictions for aplurality of targeting values, and so can be used to optimize targetingin an advertising campaign as described earlier.

The lift model may have two main components, a concentration oradvertising weight component and a targeting component. It would betypical for both of these variables to have a positive relationship withconversions. The more impressions that are generated against apopulation, the higher may be the lift from that population. Inaddition, the more targeted is the media, the more lift may begenerated.

At block 1005 of method 1000, processing logic determines the effectthat an advertising weight (e.g., a concentration or number ofadvertisements) for a product or service has on sales metrics associatedwith the product or service. For example, processing logic may determinewhether a high concentration of advertisements affected sales metricsfor a product or service (e.g., affected the number of products sold).The concentration component may be modeled as an exponential model:

$a_{1} \cdot ( {1 - \frac{1}{e^{a_{2} \cdot {I_{N}{(t)}}}}} ) \cdot k \cdot {s(t)}$where “k” may be a keep-rate and may indicate what percentage ofcustomers retain a service or subscription. Because there arediminishing returns at higher levels of advertising (e.g., higherconcentrations), an exponential model may better represent thediminishing returns. I_(N) includes the impression concentrations beinggenerated in a geographic area (national). The exponential model mayinclude a seasonal component s(t). This suggests that as sales increasedue to events in the world such as Christmas, the lift from advertisingalso increases.

At block 1010, the processing logic determines an effect that a degreeof targetedness has on the sales metrics. For example, processing logicmay determine whether selecting areas with viewers which havedemographics similar to typical buyers of a product resulted inadditional sales of the product. The second major part of the model isthe targeting component. As the media is better targeted, there ishigher lift. The targeting component may be represented as follows:(T·(θ(t)−B)+1)

At block 1015, processing logic may generate a multi-dimensional model(e.g., a landscape) of advertising effectiveness that models combinedeffects of the advertising weight and the degree of targetedness on thesales metrics. In one embodiment, the multi-dimensional model (e.g., thelandscape) may be a general function mapping any impressionconcentration (e.g., advertising weight) and degree of targetedness(e.g., tratio) to a prediction of the expected incremental sales due totelevision media on each channel. The an exponential version of the liftmodel which includes both the concentration component and the targetingcomponent as shown below:

${\%\mspace{14mu}{q_{N}(t)}} = {( {{T \cdot ( {{\theta(t)} - B} )} + 1} ) \cdot a_{1} \cdot ( {1 - \frac{1}{e^{a_{2} \cdot {I_{N}{(t)}}}}} ) \cdot k \cdot {s(t)}}$where θ(t) is the tratio for the impressions applied at time tnationally, T is the tratio slope, B is the base tratio at no targetingadvantage, a₁ is the exponential intercept and a₂ is exponential slope.Additionally, I_(N) (t) is the impression concentration applied at timet nationally in units of impressions per thousand households, k is thekeep rate (e.g., the rate in which customers retain subscriptions orservices) and s(t) represents seasonality which is the sales withoutmedia at time t2 divided by sales without media at time t1. A linearversion of the lift model may be defined as follows:% q _(N)(t)=(T·(θ(t)−B)+1)·a ₃ ·I _(N)(t)·k·s(t)

TCPM Needed to Achieve CPA Goal

It is now possible to calculate several useful quantities. The first isthe “tcpm needed for a goal” (e.g., a CPA goal). This enables allavailable media inventory to be scored, and to be compared to the tCPMthat is needed for the goal. In that way, it is then possible tocalculate the % discount that will need to be negotiated for each media,in order for it to meet a particular CPA performance goal. The tcpmneeded to achieve CPA goal may be derived from the exponential andlinear lift models to provide the formulae below:

$\mspace{79mu}{{{tcpm}^{*}( t_{2} )} = {\frac{1,000,000}{{TVHH}_{N}} \cdot {CPAGoal}^{*} \cdot {{\Delta q}_{N}/( {{\theta( t_{2} )} \cdot {I_{N}( t_{2} )}} )}}}$${{tcpm}^{*}( t_{2} )} = {1000 \cdot {CPAGoal}^{*} \cdot ( \frac{1000 \cdot a_{3}}{{TVHH}_{N}} ) \cdot k \cdot {s( t_{2} )} \cdot {q_{N}( t_{1} )} \cdot {s( t_{2} )} \cdot {( {{T \cdot ( {{\theta( t_{2} )} - B} )} + 1} )/{\theta( t_{2} )}}}$

CPM Needed to Achieve CPA Goal

The “cpm discounts needed for a goal” are also useful. These enablenegotiators and auctions systems to know what CPM they need to offer inorder for the media to meet the CPA goals. This may also be derivedusing the exponential and linear lift models. It may be useful tocalculate the discount % needed to be applied to a set of CPM prices forTV media that will allow the advertising campaign to achieve its CPAgoals. These discounts can be calculated as follows:cpm*(p)=cpm(p)*(tcpmcurrent(t2)/tcpm*(t2))% DiscountNeededForCPA(p)=100*(cpm*(p)/cpm(p)−1)

Measurement of Ad Elasticity

One type of measurement result that can be used for advertisingoptimization is a real-time advertising elasticity measurement.Elasticity estimates may be useful during a rollout when elastic changeshave been assumed. By deploying some test areas to track elasticity, itmay be possible to quantify the elastic changes and to verify that theydid or did not take place with the magnitude expected. In addition,these can be used by an advertiser to identify that they have enteredinto a higher elasticity period, in which case they may be able toexploit the current market conditions to produce greater results thanmay have previously been possible. Advertising elasticity can becalculated using the tracking cells that we have already defined asfollows:

$\begin{matrix}{ɛ = {\frac{d\mspace{14mu}\%\mspace{14mu} q_{L}}{d\; I} \cdot ( \frac{\%\mspace{14mu} q_{N}}{I_{N}} )}} \\{= {\frac{1}{J}{\sum\limits_{j = 1}^{J}\;{( \frac{\%\mspace{14mu}{q_{L}( {d_{j},t_{1},t_{2}} )}}{I_{L}( {d_{j},t_{2}} )} )( \frac{\%\mspace{14mu} q_{N}}{I_{N}} )}}}}\end{matrix}$The elasticity may change based on the current national rollout spend.However, the derivative may remain globally constant and can provide anestimate on the favorability of the current conditions, regardless ofwhat current spend is underway. An estimate of lift responsiveness toadvertising that is invariant to the current national rollout is givenby:

$\frac{d\mspace{14mu}\%\mspace{14mu} q_{L}}{d\; I} = {\frac{1}{J}{\sum\limits_{j = 1}^{J}( \frac{\%\mspace{14mu}{q_{L}( {d_{j},t_{1},t_{2}} )}}{I_{L}( {d_{j},t_{2}} )} )}}$

When measuring the advertising elasticity, local areas should bedeployed with a constant impression concentration that is maintainedthroughout the period of interest. Table 7 below illustrates anexemplary local media plan designed for elasticity measurement.

TABLE 7 Week 1 2 3 National impression concentration 600 800 200 Localimpression concentration 200 200 200

Ad elasticity can be used to take measurements of what the current localarea response to advertising is. If the environment changes, and muchhigher levels of ad elasticity are measured, then the advertiser knowsthat they can then increase their national campaign ad weight, and beable to still achieve their revenue or CPA goals. As a result, local adelasticity estimation can help to achieve significant performance gainsin a national campaign.

Halo Calculation

Another type of measurement that may be useful, and is available due tolocal lift, is the halo effect measurement. The “halo effect” may referto the propensity for TV to produce conversions that are on a wide rangeof other channels. For example, there might be 2 phone calls on TollFree Numbers, but an additional 4 conversions might occur on the web,and 1 additional phone-call to the general brand phone number.Advertisers may want to know how many additional sales are occurring onother channels.

The halo effect for phone conversions may be equal to the number ofadditional conversions that are generated following a media-generatedphone conversion and may calculated as follows:

$\begin{matrix}{{Halo} = \frac{{extra}\text{-}{conversions}\text{-}{on}\text{-}{other}\text{-}{channels}\text{-}{due}\text{-}{to}\text{-}{media}}{{phoneconversions}\text{-}{due}\text{-}{to}\text{-}{media}}} \\{= \frac{\begin{pmatrix}{{allconversions}\text{-}{on}\text{-}{all}\text{-}{channels}\text{-}{due}\text{-}{to}\text{-}{media} -} \\{{phoneconversions}\text{-}{due}\text{-}{to}\text{-}{media}}\end{pmatrix}}{{phoneconversions}\text{-}{due}\text{-}{to}\text{-}{media}}} \\{= \frac{{allconversions}\text{-}{on}\text{-}{all}\text{-}{channels}\text{-}{due}\text{-}{to}\text{-}{media}}{{{phoneconversions}\text{-}{due}\text{-}{to}\text{-}{media}} - 1}}\end{matrix}$The halo effect may be determined using two steps. First, a phoneconversions model is defined as:PhoneConversions(I)=phoneconversionsperimp*I

The phone conversion model may predict, for any number of impressions,the number of phone conversions that may be generated. The phoneconversion model may predict the number of DRTV phone conversionsgenerated for any number of impressions. Phone conversions may beuniquely tracked by 1-800 numbers that identify the station andsometimes the program which generated the call.

Next, the all conversions model is defined asAllConversions(I)=allconversionsperimp*I

Using the phone conversions model and the all conversions model, thehalo effect can be defined as:

${Halo} = {\frac{allconversionsperimp}{phoneconversionsperimp} - 1}$

Residual (Delayed Conversion) Estimates

Another useful measurement is residual estimation. Residual estimationmay indicate the number of sales which occurred after an advertisementcampaigned has stopped running (e.g., residual sales resulting from theresidual effects of the advertisement campaign).

Summary

FIG. 16 is a flow diagram illustrating a method 1600 for developing amodel for an advertisement landscape, according to another embodiment.The method 1600 may be performed by processing logic that compriseshardware (e.g., processor, circuitry, dedicated logic, programmablelogic, microcode, etc.), software (e.g., instructions run on a processorto perform hardware simulation), or a combination thereof. Theprocessing logic is configured to generate a multi-dimensional model(e.g., an advertisement landscape). In one embodiment, method 1600 maybe performed by a processor, as shown in FIG. 19.

The method 1600 starts at block 1605, where processing logic selects oneor more treatment groups using a first fitness function. The firstfitness function may evaluate the one or more treatment groups based onat least one of: advertising costs, geographic distance from othertreatment groups, population of the group, sales per capita for thegroup, difference between national census demographics and thedemographics of the group, or a degree of targetedness. In oneembodiment, the selected treatment groups include “degree oftargetedness treatment groups” and “ad weight treatment groups.” Thedegree of targetedness treatment groups will vary from control groups interms of degree of targetedness (e.g., tratio). Ad weight treatmentgroups will vary from control groups by an advertising weight. Sometreatment groups may be both degree of targetedness treatment groups andad weight treatment groups.

At block 1610, processing logic selects one or more control groups usinga second fitness function. The second fitness function may evaluate theone or more control groups based on at least one of: geographic distanceof the group from the one or more treatment groups, demographicdisparity between the group and the one or more control groups, or acable disparity between the group and the one or more treatment groups.

At block 1615, processing logic applies a baseline level of advertisingweight (e.g., a baseline concentration) to the one or more controlgroups. At block 1620, processing logic applies elevated levels ofadvertising weight for the advertisement to the one or more treatmentgroups. Different elevated levels may be applied to the different adweight treatment groups. In one embodiment, the elevated levels may behigher than the baseline levels. At block 1625, the sales metrics of theone or more control groups are compared to the sales metrics of the oneor more treatment groups.

At block 1630, processing logic determines an effect that a degree oftargetedness (e.g., a tratio) has on the sales metrics. As discussedabove, the degree of targetedness may be the probability that a sale ofa product or service will be made as a result of a view being exposed toan advertisement. For example, processing logic may determine whetherselecting areas with viewers which have demographics similar to typicalbuyers of a product, resulted in additional sales of the product. Atblock 1632, processing logic determines an effect that the advertisingweight has on the sales metrics. At block 1635, processing logic maygenerate a multi-dimensional model (e.g., a landscape) of advertisingeffectiveness that models combined effects of the advertising weight andthe degree of targetedness on the sales metrics. Multiple differenttypes of multi-dimensional models have been discussed above withreference to FIG. 15.

At block 1640, processing logic may apply the multi-dimensional model toevaluate an advertisement campaign. For example, processing logic mayinput one or more parameters for a national ad campaign into themulti-dimensional model to receive an output. The output may identifypredicted sales metrics based on the input parameters. Additionally, themulti-dimensional model may be used to account for differences intargetedness and advertising weights between a national ad campaign andexperimental local ad campaigns.

In one embodiment, processing logic may combine the multi-dimensionalmodel with inventory availability data (e.g., data about the currentstock of inventory in one or more areas) and may predict the futureinventors for the product or service based on a particular advertisingweight and a particular degree of targetedness, using themulti-dimensional model. For example, if the user selects a differentadvertising weight and a different degree of targetedness, processinglogic may predict the change in inventory level (e.g., the amount ofincrease or reduction in the inventory level) based on the advertisingweight and a different degree of targetedness. In another example, ifadvertising weight (e.g., concentration) is increased and degree oftargetedness is increased, this may result in a higher amount of sales,and processing logic may predict that the future inventory will belower, due to the increased sales.

In another embodiment, processing logic may provide a user interfacewith controls to adjust at least one of the advertising budget, theadvertising weight, and the degree of targetedness. Processing logic mayreceive an adjustment to one or more of the advertising budget, theadvertising weight, and the degree of targetedness, and may providepredictions of future sales metrics based on the adjustment. Forexample, processing logic may receive an adjustment lowering theadvertising weight (e.g., the concentration) and may predict that saleswill drop by a certain amount due to the decrease in advertising weight.

FIG. 17 is a flow diagram illustrating a method 1700 for optimizing amedia campaign, according to one embodiment. The method 1700 may beperformed by processing logic that comprises hardware (e.g., processor,circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processor to perform hardwaresimulation), or a combination thereof. The processing logic isconfigured to optimize a media campaign. In one embodiment, method 1700may be performed by a processor, as shown in FIG. 19.

The method 1700 starts at block 1705, where processing logic tracks oneor more sales metrics (e.g., sales performance) for a product or servicebased on differences between a first advertising result for a controlgroup and a second advertisement result for a treatment group. Thecontrol group is subject to a first advertisement campaign and thetreatment group is subject to the first advertisement campaign and to asecond advertisement campaign. For example, processing logic may trackthe cost per impression (cpi), or the cost per acquisition (cpa), etc.At block 1710, processing logic compares the one or more sales metricsto one or more sales goals for the product or service. For example,processing logic may determine whether the cpa for the treatment groupis below a certain threshold.

At block 1715, processing logic identifies an adjustment to the firstadvertisement campaign that will cause the one or more sales metrics tomore closely meet the one or more sales goals. For example, if a salesgoal is to have 100 sales of a product, and only 55 products have beensold, the processing logic may determine that the advertisement weightof the first advertisement campaign should be adjusted (e.g., theadvertisement weight or concentration should be increased to generatemore impressions which may result in more sales). At block 1720,processing logic performs the adjustment to optimize the firstadvertisement campaign (e.g., to cause the one or more sales metrics tochange such that they are closer to the one or sales goals).

In one embodiment, processing logic may automatically reduce theadvertising weight if it is determined that the CPI of the firstadvertisement campaign is below a first threshold, or may automaticallyincrease the advertising if the CPI of the first advertisement campaignis above the first threshold or a second threshold. In anotherembodiment, processing logic may automatically adjust the degree oftargetedness for the first advertisement campaign. Processing logic mayadditionally make a prediction of an advertising weight that will causethe one or more sales metrics to meet the one or more sales goals andmay automatically adjust the advertisement weight, based on theprediction.

In one embodiment, processing logic may test an adjustment to the firstadvertisement campaign by applying the adjustment to the secondadvertisement campaign. Processing logic may compare a new advertisingresult associated with the treatment group with the first advertisingresult and may apply the adjustment if the new advertising result issuperior to or better than the first advertising result. In anotherembodiment, processing logic measures changes in a difference betweenthe first advertising result and the second advertising result thatoccur without changes to the first advertisement campaign or the secondadvertisement campaign.

At block 1725, processing logic monitors a change in the sales metricsthat occurs in response to the adjustment that was made at block 1720.At block 1740, processing logic determines whether the updated salesmetrics meet the sales goals. If not, the method returns to block 1715to perform an additional adjustment to the first advertisement campaign.If the sales metrics meet the sales goals, then the method may end.

FIG. 18 is a flow diagram illustrating a method 1800 for optimizing amedia campaign, according to another embodiment. The method 1800 may beperformed by processing logic that comprises hardware (e.g., processor,circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processor to perform hardwaresimulation), or a combination thereof. The processing logic isconfigured to optimize a media campaign. In one embodiment, method 1800may be performed by a processor, as shown in FIG. 19.

The method 1800 starts at block 1805, where processing logic tracks oneor more sales metrics for a product or service during an advertisementcampaign. For example, processing logic may track the total amount ofsales, the costs of the advertisement campaign, etc. At block 1810,processing logic compares the one or more sales metrics to the one ormore sales goals for the product or service. For example, processinglogic may determine whether total amount of sales meets a sales goal. Inanother example, processing logic may determine whether the cost of theadvertisement campaign exceeds a certain cost.

At block 1815, if the one or more sales metrics surpass the one or moregoals by a threshold amount, processing logic may decrease the amount oftargetedness for the advertisement campaign. For example, if the costper impression is below a target (e.g., is lower than the goal), theprocessing logic may decrease the targetedness of the advertisementcampaign. At block 1820, if the one or more sales metrics falls short ofthe one or more goals by a threshold amount, processing logic mayincrease the amount of targetedness for the advertisement campaign. Forexample, if the cost per impression is above the target cost perimpression, processing logic may increase the targetedness of theadvertisement campaign. After block 1820, the method ends.

FIGS. 2, 8A-8B, 10 and 16-18 are flow diagrams illustrating methods fortracking and managing advertisement campaigns. For simplicity ofexplanation, the methods are depicted and described as a series of acts.However, acts in accordance with this disclosure can occur in variousorders and/or concurrently, and with other acts not presented anddescribed herein. Furthermore, not all illustrated acts may be requiredto implement the methods in accordance with the disclosed subjectmatter. In addition, those skilled in the art will understand andappreciate that the methods could alternatively be represented as aseries of interrelated states via a state diagram or events.

FIG. 19 illustrates a diagrammatic representation of a machine in theexemplary form of a computer system 1900 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed. The system 1900 may bein the form of a computer system within which a set of instructions, forcausing the machine to perform any one or more of the methodologiesdiscussed herein, may be executed. In alternative embodiments, themachine may be connected (e.g., networked) to other machines in a LAN,an intranet, an extranet, or the Internet. The machine may operate inthe capacity of a server machine in client-server network environment.The machine may be a personal computer (PC), a set-top box (STB), aserver, a network router, switch or bridge, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein.

The exemplary computer system 1900 includes a processing device (e.g.,one or more processors) 1902, a main memory 1904 (e.g., read-only memory(ROM), flash memory, dynamic random access memory (DRAM) such assynchronous DRAM (SDRAM)), a static memory 1906 (e.g., flash memory,static random access memory (SRAM)), and a data storage device 1918,which communicate with each other via a bus 1930.

Processing device 1902 represents one or more general-purpose processorssuch as a microprocessor, central processing unit, or the like. Moreparticularly, the processing device 1902 may be a complex instructionset computing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,or a processor implementing other instruction sets or processorsimplementing a combination of instruction sets. The processing device1902 may also be one or more special-purpose processing devices such asan application specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. The processing device 1902 is configured to execute theinstructions 1926 for performing the operations and steps discussedherein.

The computer system 1900 may further include a network interface device1908 which may communicate with a network 1920. The computer system 1900also may include a video display unit 1910 (e.g., a liquid crystaldisplay (LCD) or a cathode ray tube (CRT)), an alphanumeric input device1912 (e.g., a keyboard), a cursor control device 1914 (e.g., a mouse, atouch screen, a touch pad, a stylus, etc.), and a signal generationdevice 1916 (e.g., a speaker).

The data storage device 1918 may include a computer-readable storagemedium 1928 on which is stored one or more sets of instructions (e.g.,instructions 1926 for an analytics engine 1990) embodying any one ormore of the methodologies or functions described herein. Theinstructions 1926 may also reside, completely or at least partially,within the main memory 1904 and/or within the processing device 1902during execution thereof by the computer system 1900, the main memory1904 and the processing device 1902 also constituting computer-readablemedia. The instructions may further be transmitted or received over anetwork 1920 via the network interface device 1908.

While the computer-readable storage medium 1928 is shown in an exemplaryembodiment to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The term“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present invention.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical media,and magnetic media.

In the above description, numerous details are set forth. It will beapparent, however, to one of ordinary skill in the art having thebenefit of this disclosure, that embodiments of the invention may bepracticed without these specific details. In some instances, well-knownstructures and devices are shown in block diagram form, rather than indetail, in order to avoid obscuring the description.

Some portions of the detailed description are presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as “receiving,” “generating,” “determining,” “calculating,”“introducing,” “providing,” “selecting,” “updating,” “adjusting,”“modifying,” “computing,” “using,” “applying,” “comparing,” “analyzing,”“tracking,” “incorporating,” “combining,” “predicting,” “performing,”“reducing,” “increasing,” “making,” “monitoring,” “maintaining,”“updating,” “testing,” “measuring,” “identifying,” or the like, refer tothe actions and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (e.g., electronic) quantities within the computer system'sregisters and memories into other data similarly represented as physicalquantities within the computer system memories or registers or othersuch information storage, transmission or display devices.

Embodiments of the invention also relate to an apparatus for performingthe operations herein. This apparatus may be specially constructed forthe required purposes, or it may comprise a general purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but not limited to, any type of diskincluding floppy disks, optical disks, CD-ROMs, and magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical media, or any type of media suitable forstoring electronic instructions.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct a more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will appear from the description below.In addition, the present invention is not described with reference toany particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof the invention as described herein.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Many other embodiments will beapparent to those of skill in the art upon reading and understanding theabove description. The scope of the invention should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A method comprising: selecting, by a computingdevice, a control population of persons; selecting, by the computingdevice, a targeted population of persons, the targeted population ofpersons comprising different people than the control population ofpersons; broadcasting, on a broadcast medium, a first plurality of mediadata elements, the first plurality of media data elements beingassociated with a product or service, to the control population ofpersons such that the control population of persons receives andconsumes the first plurality of media data elements; broadcasting, on abroadcast medium, a second plurality of media data elements, the secondplurality of media data elements being associated with a product orservice, to the targeted population of persons such that the targetedpopulation of persons receives and consumes the second plurality ofmedia data elements; measuring control conversion metrics of the productor service associated with the control population of persons over a timeperiod, wherein the control conversion metrics comprise at least one ofconversions per capita, probability of conversion during the timeperiod, or conversions per time period; measuring target conversionmetrics of the product or service associated with the targetedpopulation of persons over the time period, wherein the targetconversion metrics comprise at least one of conversions per capita,probability of conversion during the time period, or conversions pertime period; determining a control media impression concentration as anextent of the media exposure that the control population has receivedfor the product or service over the time period, wherein the controlmedia impression concentration is based on a number of impressionsdelivered to the control populations of persons; determining a targetmedia impression concentration as an extent of the media exposure thatthe targeted population of persons has received for the product orservice over the time period, wherein the target media impressionconcentration is based on a number of impressions delivered to thetargeted population of persons; determining, by comparing the controlmedia impression concentration with the target media impressionconcentration, an effect that the target media impression concentrationof the second plurality of media data elements has on the targetconversion metrics; determining, by the computing device, a controlprobability of conversion for the first plurality of media data elementsto the control population of persons, the control probability ofconversion corresponding to a likelihood that members of the controlpopulation of persons will convert on the product or service;determining, by the computing device, a target probability of conversionfor the second plurality of media data elements to the targetedpopulation of persons by: determining whether the target conversionmetrics exceed a predetermined threshold; upon determining that thetarget conversion metrics exceed the predetermined threshold,determining the target probability of conversion as a ratio of thetarget conversion metrics and a number of exposures to the secondplurality of media data elements; and upon determining that the targetconversion metrics do not exceed the predetermined threshold,determining the target probability of conversion based on a comparisonof demographics the targeted population of persons with demographics ofpurchasers of the product or service; modifying the target mediaimpression concentration and/or the target probability of conversion toimprove one or more target conversion metrics; generating, by thecomputing device, a multi-dimensional model that combines and measuresthe determined effects of the target media impression concentration andthe target probability of conversion on the target conversion metrics;combining conversion estimates generated based on the multi-dimensionalmodel with inventory quantity availability data for the product orservice; and predicting future inventory quantities for the product orservice based on a planned application of the target media impressionconcentration and a particular target probability of conversion to apopulation in a given time period.
 2. The method of claim 1, furthercomprising: selecting the targeted population of persons using a firstfitness function that evaluates a suitability of a group of personsbased on at least one of media cost associated with the group,geographic distance of the group from other targeted population groups,conversions per capita for the group, difference between a nationalcensus demographic average and demographics of the group, or a targetprobability of conversion; and selecting the control population ofpersons using a second fitness function that evaluates a suitability ofa group of persons based on at least one of geographic distance of thegroup from the one or more targeted population of persons, demographicdisparity between the group and other control populations of persons, ora cable penetration disparity between the group and the one or moretargeted population.
 3. The method of claim 1, further comprising:analyzing historical conversion for the target population of persons todetermine a historical variability in target conversion metrics for thetarget population of persons; and determining the elevated levels oftarget media impression concentration that will cause the second targetconversion metrics to be outside of the historical variability.
 4. Themethod of claim 1, further comprising: after a time period, reducing thetarget media impression concentration of the second plurality of mediadata elements for the targeted population to the baseline level;tracking an amount of time that it takes for the second targetconversion metrics to decline to levels of the first target conversionmetrics; determining residual conversion associated with the elevatedlevels of target media impression concentration based on the determinedamount of time; and incorporating the residual conversion into themulti-dimensional model.
 5. The method of claim 1, wherein modifying thetarget probability of conversion comprises: selecting a first targetedpopulation of persons with a high target probability of conversion amongthe targeted population of persons, applying a particular target mediaimpression concentration to that the first targeted population ofpersons, and measuring target conversion metrics from that the firsttargeted population of persons; selecting a second targeted populationof persons with a low target probability of conversion among thetargeted population of persons, applying the particular target mediaimpression concentration to that the second targeted population ofpersons, and measuring second target conversion metrics from the secondtargeted population of persons; and comparing the target conversionmetrics to the second target conversion metrics.
 6. The method of claim5, further comprising: selecting the first targeted population ofpersons using a first fitness function that evaluates a suitability of agroup for use as a control population based on at least one ofgeographic distance of the group from the targeted population, matchedmovement of target conversion metrics to the one or more target groups,demographic disparity between the group and the targeted population, ora cable penetration disparity between the group and the targetedpopulation; and selecting the second targeted population of personsusing a second fitness function that evaluates a suitability of a groupfor use as a targeted population based on at least one of media costassociated with the group, geographic distance of the group from othertargeted populations, population of the group, conversions per capitafor the group, difference between national census demographic averageand demographics of the group, or a degree of correlation between theconsumer demographics and the viewer demographics for the group.
 7. Anon-transitory computer readable storage medium having instructionsthat, when executed by a computing device, cause the computing device toperform operations comprising: selecting, by the computing device, acontrol population of persons; selecting, by the computing device, atargeted population of persons, the targeted population of personscomprising different people than the control population of persons;broadcasting, on a broadcast medium, a first plurality of media dataelements, the first plurality of media data elements being associatedwith a product or service, to the control population of persons suchthat the control population of persons receives and consumes the firstplurality of media data element; broadcasting, on a broadcast medium, asecond plurality of media data elements, the second plurality of mediadata elements being associated with a product or service, to thetargeted population of persons such that the targeted population ofpersons receives and consumes the second plurality of media dataelements; measuring control conversion metrics of the product or serviceassociated with the control population of persons over a time period,wherein the control conversion metrics comprise at least one ofconversions per capita, probability of conversion during the timeperiod, or conversions per time period; measuring target conversionmetrics of the product or service associated with the targetedpopulation of persons over the time period, wherein the targetconversion metrics comprise at least one of conversions per capita,probability of conversion during the time period, or conversions pertime period; determining a control media impression concentration as anextent of the media exposure that the control population has receivedfor the product or service over the time period, wherein the controlmedia impression concentration is based on a number of impressionsdelivered to the control populations of persons; determining a targetmedia impression concentration as an extent of the media exposure thatthe targeted population of persons has received for the product orservice over the time period, wherein the target media impressionconcentration is based on a number of impressions delivered to thetargeted population of persons; determining, by comparing the controlmedia impression concentration with the target media impressionconcentration, an effect that the target media impression concentrationof the second plurality of media data elements has on the targetconversion metrics; determining, by the computing device, a controlprobability of conversion for the first plurality of media data elementsto the control population of persons, the control probability ofconversion corresponding to a likelihood that members of the controlpopulation of persons will convert on the product or service;determining, by the computing device, a target probability of conversionfor the second plurality of media data elements to the targetedpopulation of persons by: determining whether the target conversionmetrics exceed a predetermined threshold; upon determining that thetarget conversion metrics exceed the predetermined threshold, determinethe target probability of conversion as a ratio of the target conversionmetrics and a number of exposures to the second plurality of media dataelements; and upon determining that the target conversion metrics do notexceed the predetermined threshold, determine the target probability ofconversion based on a comparison of demographics the targeted populationof persons with demographics of purchasers of the product or service;modifying the target media impression concentration and/or the targetprobability of conversion to improve one or more target conversionmetrics; generating, by the computing device, a multi-dimensional modelthat combines and measures the determined effects of the target mediaimpression concentration and the target probability of conversion on thetarget conversion metrics; combining conversion estimates generatedbased on the multi-dimensional model with inventory quantityavailability data for the product or service; and predicting futureinventory quantities for the product or service based on a plannedapplication of the target media impression concentration and aparticular target probability of conversion to a population in a giventime period.
 8. The non-transitory computer readable storage medium ofclaim 7, the operations further comprising: selecting the targetedpopulation of persons using a first fitness function that evaluates asuitability of a group of persons based on at least one of media costassociated with the group, geographic distance of the group from othertargeted population groups, conversions per capita for the group,difference between a national census demographic average anddemographics of the group, or a target probability of conversion; andselecting the control population of persons using a second fitnessfunction that evaluates a suitability of a group of persons based on atleast one of geographic distance of the group from the one or moretargeted population of persons, demographic disparity between the groupand other control populations of persons, or a cable penetrationdisparity between the group and the one or more targeted population. 9.The non-transitory computer readable storage medium of claim 7, theoperations further comprising: analyzing historical conversion for thetarget population of persons to determine a historical variability intarget conversion metrics for the target population of persons; anddetermining the elevated levels of target media impression concentrationthat will cause the second target conversion metrics to be outside ofthe historical variability.
 10. The non-transitory computer readablestorage medium of claim 7, the operations further comprising: after atime period, reducing the target media impression concentration of thesecond plurality of media data elements for the targeted population tothe baseline level; tracking an amount of time that it takes for thesecond target conversion metrics to decline to levels of the firsttarget conversion metrics; determining residual conversion associatedwith the elevated levels of target media impression concentration basedon the determined amount of time; and incorporating the residualconversion into the multi-dimensional model.
 11. The non-transitorycomputer readable storage medium of claim 7, wherein modifying thetarget probability of conversion comprises: selecting a first targetedpopulation of persons with a high target probability of conversion amongthe targeted population of persons, applying a particular target mediaimpression concentration to that the first targeted population ofpersons, and measuring target conversion metrics from that the firsttargeted population of persons; selecting a second targeted populationof persons with a low target probability of conversion among thetargeted population of persons, applying the particular target mediaimpression concentration to that the second targeted population ofpersons, and measuring second target conversion metrics from the secondtargeted population of persons; and comparing the target conversionmetrics to the second target conversion metrics.
 12. The non-transitorycomputer readable storage medium of claim 11, the operations furthercomprising: selecting the first targeted population of persons using afirst fitness function that evaluates a suitability of a group for useas a control population based on at least one of geographic distance ofthe group from the targeted population, matched movement of targetconversion metrics to the one or more target groups, demographicdisparity between the group and the targeted population, or a cablepenetration disparity between the group and the targeted population; andselecting the second targeted population of persons using a secondfitness function that evaluates a suitability of a group for use as atargeted population based on at least one of media cost associated withthe group, geographic distance of the group from other targetedpopulations, population of the group, conversions per capita for thegroup, difference between national census demographic average anddemographics of the group, or a degree of correlation between theconsumer demographics and the viewer demographics for the group.
 13. Acomputing device comprising: a memory to store instructions forgenerating a multi-dimensional model of media effectiveness; and aprocessing device coupled to the memory, to execute the instructions,wherein the processing device is configured to execute a methodcomprising: selecting, by the computing device, a control population ofpersons; selecting, by the computing device, a targeted population ofpersons, the targeted population of persons comprising different peoplethan the control population of persons; broadcasting, on a broadcastmedium, a first plurality of media data elements, the first plurality ofmedia data elements being associated with a product or service, to thecontrol population of persons such that the control population ofpersons receives and consumes the first plurality of media data element;broadcasting, on a broadcast medium, a second plurality of media dataelements, the second plurality of media data elements being associatedwith a product or service, to the targeted population of persons suchthat the targeted population of persons receives and consumes the secondplurality of media data elements; measuring control conversion metricsof the product or service associated with the control population ofpersons over a time period, wherein the control conversion metricscomprise at least one of conversions per capita, probability ofconversion during the time period, or conversions per time period;measuring target conversion metrics of the product or service associatedwith the targeted population of persons over the time period, whereinthe target conversion metrics comprise at least one of conversions percapita, probability of conversion during the time period, or conversionsper time period; determining a control media impression concentration asan extent of the media exposure that the control population has receivedfor the product or service over the time period, wherein the controlmedia impression concentration is based on a number of impressionsdelivered to the control populations of persons; determining a targetmedia impression concentration as an extent of the media exposure thatthe targeted population of persons has received for the product orservice over the time period, wherein the target media impressionconcentration is based on a number of impressions delivered to thetargeted population of persons; determining, by comparing the controlmedia impression concentration with the target media impressionconcentration, an effect that the target media impression concentrationof the second plurality of media data elements has on the targetconversion metrics; determining, by the computing device, a controlprobability of conversion for the first plurality of media data elementsto the control population of persons, the control probability ofconversion corresponding to a likelihood that members of the controlpopulation of persons will convert on the product or service;determining, by the computing device, a target probability of conversionfor the second plurality of media data elements to the targetedpopulation of persons by: determining whether the target conversionmetrics exceed a predetermined threshold; upon determining that thetarget conversion metrics exceed the predetermined threshold, determinethe target probability of conversion as a ratio of the target conversionmetrics and a number of exposures to the second plurality of media dataelements; and upon determining that the target conversion metrics do notexceed the predetermined threshold, determine the target probability ofconversion based on a comparison of demographics the targeted populationof persons with demographics of purchasers of the product or service;modifying the target media impression concentration and/or the targetprobability of conversion to improve one or more target conversionmetrics; generating, by the computing device, the multi-dimensionalmodel that combines and measures the determined effects of the targetmedia impression concentration and the target probability of conversionon the target conversion metrics; combining conversion estimatesgenerated based on the multi-dimensional model with inventory quantityavailability data for the product or service; and predicting futureinventory quantities for the product or service based on a plannedapplication of the target media impression concentration and aparticular target probability of conversion to a population in a giventime period.
 14. The computing device of claim 13, wherein themulti-dimensional model is a two-dimensional model.